WQD7004 - Group Project - Weather Forecasting
Group Members
1. 22099247 Wang Youqing
2. 22070924 He Xiaofeng
3. S2176972 Muhammad Nasrullah
4. S2169356 Nur Izzah Athirah Alzahri
5. 22056889 Rayan Omar Abuhasha
1 Data Understanding
1.1 Introduction
Kuala Lumpur, the capital and geographic centre of Malaysia, is a thriving metropolis that sees a daily influx of numerous economic activity. Even though Kuala Lumpur is a very cosmopolitan city, it is but not immune to the many, erratic natural disasters that, among other things, bring down the spirits of countless industrious Malaysians through torrential downpours, typhoons, and flash floods. The government organisation in charge of weather forecasting in this nation is the Malaysian Meteorological Department, or MET Malaysia. Over the past ten years, meteorology has become increasingly data-centric, necessitating the need for more advanced techniques for analysing and interpreting weather data. Complex machine learning approaches are preferred over their traditional counterparts to handle this new data boom, as they are better able to capture the relationships between different weather variables in situations where there is a large amount of data, which can be difficult with traditional methods. It appears that a large number of research papers have used machine learning simulations for weather forecasting, but very few have produced a useful data result. The main motivation for this group project is the requirement to create a data product that can reliably predict and forecast the weather in Kuala Lumpur, Malaysia. weather prediction and forecasting are crucial aspects of daily life, more so in areas where weather patterns are highly unpredictable such as in Kuala Lumpur, Malaysia.Accurate and timely predictions of weather conditions are essential for a range of economic and non-economic activities. However, despite advances in weather forecasting technology, predicting weather with high accuracy remains a challenging problem to this present day, but a problem we wish to tackle in this group project.
1.2 Project Objective
To Develop an Accurate Rainfall Prediction Model
To Evaluate Model Stability and Accuracy
To Provide Practical Forecasts for Users
To Create a Shiny Data Product for Instant Weather Forecasts
1.3 Data Background
Title: weather_forecasting_data
Year: 2022-2023
Purpose: forecast weather conditions in Kuala Lumpur
Source: https://www.visualcrossing.com/weather/weather-data-services/kuala%20lumpur
1.4 Import the packages before starting with data preprocessing
##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union1.5 Read the original dataset
origenal_data <- read_excel("/Users/hexiaofeng/Downloads/WQD7004_Group Assignment/weather_forecasting_data.xlsx")
head(origenal_data)## # A tibble: 6 × 24
## name datetime temp feelslike dew humidity precip precipprob
## <chr> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 kuala lu… 2022-03-01 00:00:00 26.2 26.2 22.9 81.8 0 0
## 2 kuala lu… 2022-03-01 01:00:00 26.1 26.1 23.9 87.5 0 0
## 3 kuala lu… 2022-03-01 02:00:00 25.9 25.9 23.8 88.0 0 0
## 4 kuala lu… 2022-03-01 03:00:00 26.1 26.1 23.8 86.9 0 0
## 5 kuala lu… 2022-03-01 04:00:00 25.2 25.2 23.8 91.6 0 0
## 6 kuala lu… 2022-03-01 05:00:00 24.9 24.9 23.7 92.8 0 0
## # ℹ 16 more variables: preciptype <chr>, snow <dbl>, snowdepth <dbl>,
## # windgust <dbl>, windspeed <dbl>, winddir <dbl>, sealevelpressure <dbl>,
## # cloudcover <dbl>, visibility <dbl>, solarradiation <dbl>,
## # solarenergy <dbl>, uvindex <dbl>, severerisk <dbl>, conditions <chr>,
## # icon <chr>, stations <chr>1.6 Load all library
library(pryr)
library(Rcpp)
library(ggplot2)
library(lubridate)
library(tidyr)
library(caret)
library(randomForest)
library(devtools)
library(infotheo)
library(corrplot)
library(reshape2)
library(coefplot)
library(forecast)
library(writexl)
library(tseries)Load data
excel_file = "/Users/hexiaofeng/Downloads/WQD7004_Group Assignment/weather_forecasting_data.xlsx"
# get the names of all sheets in the file
sheet_names = excel_sheets(excel_file)2 Data Preparation
2.1 Create an empty data frame to store the combined data
combined_data = data.frame()2.2 Loop through all sheets and combine them into the data frame
for (sheet in sheet_names) {
sheet_data = read_excel(excel_file, sheet = sheet)
print(sheet)
sheet_data$datetime <- gsub("T", " ", sheet_data$datetime)
sheet_data$datetime <- as.POSIXct(sheet_data$datetime, format = "%Y-%m-%d %H:%M:%S")
sheet_data$temp <- as.numeric(sheet_data$temp)
sheet_data$feelslike <- as.numeric(sheet_data$feelslike)
sheet_data$dew <- as.numeric(sheet_data$dew)
sheet_data$humidity <- as.numeric(sheet_data$humidity)
sheet_data$precip <- as.numeric(sheet_data$precip)
sheet_data$precipprob <- as.numeric(sheet_data$precipprob)
sheet_data$snow <- as.numeric(sheet_data$snow)
sheet_data$snowdepth <- as.numeric(sheet_data$snowdepth)
sheet_data$windgust <- as.numeric(sheet_data$windgust)
sheet_data$windspeed <- as.numeric(sheet_data$windspeed)
sheet_data$winddir <- as.numeric(sheet_data$winddir)
sheet_data$sealevelpressure <- as.numeric(sheet_data$sealevelpressure)
sheet_data$cloudcover <- as.numeric(sheet_data$cloudcover)
sheet_data$visibility <- as.numeric(sheet_data$visibility)
sheet_data$solarradiation <- as.numeric(sheet_data$solarradiation)
sheet_data$solarenergy <- as.numeric(sheet_data$solarenergy)
sheet_data$uvindex <- as.numeric(sheet_data$uvindex)
sheet_data$severerisk <- as.numeric(sheet_data$severerisk)
combined_data = bind_rows(combined_data, sheet_data)
}## [1] "Mac 22"
## [1] "Apr 22"
## [1] "May 22"
## [1] "Jun 22"
## [1] "Jul 22"
## [1] "Aug 22"
## [1] "Sep 22"
## [1] "Oct 22"
## [1] "Nov 22"
## [1] "Dec 22"
## [1] "Jan 23"
## [1] "Feb 23"
## [1] "Mac 23"2.3 Drop not make sense columns
dataframe = subset(combined_data, select = -c(name, conditions, icon, stations))2.4 View the content of the data
names(dataframe)## [1] "datetime" "temp" "feelslike" "dew"
## [5] "humidity" "precip" "precipprob" "preciptype"
## [9] "snow" "snowdepth" "windgust" "windspeed"
## [13] "winddir" "sealevelpressure" "cloudcover" "visibility"
## [17] "solarradiation" "solarenergy" "uvindex" "severerisk"object_size(dataframe)## 1.52 MBstr(dataframe)## 'data.frame': 9504 obs. of 20 variables:
## $ datetime : POSIXct, format: NA "2022-03-01 01:00:00" ...
## $ temp : num 26.2 26.1 25.9 26.1 25.2 24.9 25 25 25 25.1 ...
## $ feelslike : num 26.2 26.1 25.9 26.1 25.2 24.9 25 25 25 25.1 ...
## $ dew : num 22.9 23.9 23.8 23.8 23.8 23.7 24 24 23.9 24 ...
## $ humidity : num 81.8 87.5 88 86.9 91.6 ...
## $ precip : num 0 0 0 0 0 0 0 0 0 0 ...
## $ precipprob : num 0 0 0 0 0 0 0 0 0 0 ...
## $ preciptype : chr NA NA NA NA ...
## $ snow : num 0 0 0 0 0 0 0 0 0 0 ...
## $ snowdepth : num 0 0 0 0 0 0 0 0 0 0 ...
## $ windgust : num 9.4 8.6 7.9 8.3 7.9 8.3 8.3 8.6 8.3 5.8 ...
## $ windspeed : num 6.7 4.3 8.5 8.4 1.1 3.8 0.4 2.5 0 0 ...
## $ winddir : num 5 40 322 329 356 45 0 270 0 0 ...
## $ sealevelpressure: num 1011 1011 1010 1010 1010 ...
## $ cloudcover : num 27.5 27.5 57.3 27.5 27.5 57.3 27.5 30.1 58.7 30.1 ...
## $ visibility : num 10 10 10 10 8.2 6.5 8.2 7.2 10 8.2 ...
## $ solarradiation : num 0 0 NA 0 0 0 0 0 NA 208 ...
## $ solarenergy : num NA NA NA NA NA NA NA NA NA 0.7 ...
## $ uvindex : num 0 0 NA 0 0 0 0 0 NA 2 ...
## $ severerisk : num 3 10 10 10 3 3 3 3 5 10 ...summary(dataframe)## datetime temp feelslike dew
## Min. :2022-03-01 01:00:00 Min. :22.3 Min. :22.30 Min. :14.60
## 1st Qu.:2022-06-08 00:30:00 1st Qu.:25.8 1st Qu.:25.80 1st Qu.:23.00
## Median :2022-09-15 00:00:00 Median :27.0 Median :30.30 Median :23.90
## Mean :2022-09-15 00:00:00 Mean :27.8 Mean :30.39 Mean :23.61
## 3rd Qu.:2022-12-22 23:30:00 3rd Qu.:29.9 3rd Qu.:34.50 3rd Qu.:24.10
## Max. :2023-03-31 23:00:00 Max. :35.8 Max. :43.60 Max. :26.90
## NA's :396
## humidity precip precipprob preciptype
## Min. : 33.19 Min. : 0.0000 Min. : 0.000 Length:9504
## 1st Qu.: 69.60 1st Qu.: 0.0000 1st Qu.: 0.000 Class :character
## Median : 83.24 Median : 0.0000 Median : 0.000 Mode :character
## Mean : 79.26 Mean : 0.2727 Mean : 3.967
## 3rd Qu.: 89.37 3rd Qu.: 0.0000 3rd Qu.: 0.000
## Max. :100.00 Max. :70.1270 Max. :100.000
##
## snow snowdepth windgust windspeed winddir
## Min. :0 Min. :0 Min. : 0.400 Min. : 0.000 Min. : 0
## 1st Qu.:0 1st Qu.:0 1st Qu.: 3.200 1st Qu.: 1.600 1st Qu.: 10
## Median :0 Median :0 Median : 4.700 Median : 4.800 Median :150
## Mean :0 Mean :0 Mean : 5.836 Mean : 5.429 Mean :151
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.: 7.600 3rd Qu.: 8.300 3rd Qu.:280
## Max. :0 Max. :0 Max. :53.600 Max. :27.400 Max. :360
##
## sealevelpressure cloudcover visibility solarradiation
## Min. :1003 Min. :25.00 Min. : 0.900 Min. : 0.0
## 1st Qu.:1008 1st Qu.:27.50 1st Qu.: 9.700 1st Qu.: 0.0
## Median :1009 Median :30.10 Median :10.000 Median : 7.0
## Mean :1009 Mean :42.92 Mean : 9.623 Mean : 219.4
## 3rd Qu.:1011 3rd Qu.:57.30 3rd Qu.:10.000 3rd Qu.: 422.0
## Max. :1017 Max. :90.00 Max. :11.400 Max. :1052.0
## NA's :27
## solarenergy uvindex severerisk
## Min. :0.000 Min. : 0.000 Min. : 3.00
## 1st Qu.:0.400 1st Qu.: 0.000 1st Qu.:10.00
## Median :1.400 Median : 0.000 Median :10.00
## Mean :1.489 Mean : 2.179 Mean :16.29
## 3rd Qu.:2.500 3rd Qu.: 4.000 3rd Qu.:30.00
## Max. :3.800 Max. :10.000 Max. :75.00
## NA's :4480 NA's :272.5 Check data types, distribution, weird number
data_profiling(dataframe)
2.6 Not useful (cant use to differentiate preciptype), drop
As we can see from the plot above, the snow and snowdepth columns have low correlations with other variables and are unlikely to have a significant impact on the performance of the model. So drop it
dataframe = subset(dataframe, select = -c(snow, snowdepth))2.7 Check na occurrence
## datetime temp feelslike dew
## 396 0 0 0
## humidity precip precipprob preciptype
## 0 0 0 7490
## windgust windspeed winddir sealevelpressure
## 0 0 0 0
## cloudcover visibility solarradiation solarenergy
## 0 0 27 4480
## uvindex severerisk
## 27 0
2.8 Less than 10%, replace all
- Missing values in the ‘solarradiation’ column are replaced with the mean of non-missing values in column.
- Missing values in the ‘uvindex’ column are replaced with the mean of non-missing values in that column.
- Missing values in the ‘preciptype’ column are replaced with the string “no rain.”
dataframe$solarradiation = replace(dataframe$solarradiation, is.na(dataframe$solarradiation), mean(dataframe$solarradiation, na.rm = TRUE))
dataframe$uvindex = replace(dataframe$uvindex, is.na(dataframe$uvindex), mean(dataframe$uvindex, na.rm = TRUE))
dataframe$preciptype = replace(dataframe$preciptype, is.na(dataframe$preciptype), "no rain")2.9 Too much null, drop
dataframe = subset(dataframe, select = -c(solarenergy))2.10 Check na occurrence again
null_counts=colSums(is.na(dataframe))
print(null_counts)## datetime temp feelslike dew
## 396 0 0 0
## humidity precip precipprob preciptype
## 0 0 0 0
## windgust windspeed winddir sealevelpressure
## 0 0 0 0
## cloudcover visibility solarradiation uvindex
## 0 0 0 0
## severerisk
## 02.11 Re-check
print(unique(dataframe$preciptype))## [1] "no rain" "rain"print(str(dataframe))## 'data.frame': 9504 obs. of 17 variables:
## $ datetime : POSIXct, format: NA "2022-03-01 01:00:00" ...
## $ temp : num 26.2 26.1 25.9 26.1 25.2 24.9 25 25 25 25.1 ...
## $ feelslike : num 26.2 26.1 25.9 26.1 25.2 24.9 25 25 25 25.1 ...
## $ dew : num 22.9 23.9 23.8 23.8 23.8 23.7 24 24 23.9 24 ...
## $ humidity : num 81.8 87.5 88 86.9 91.6 ...
## $ precip : num 0 0 0 0 0 0 0 0 0 0 ...
## $ precipprob : num 0 0 0 0 0 0 0 0 0 0 ...
## $ preciptype : chr "no rain" "no rain" "no rain" "no rain" ...
## $ windgust : num 9.4 8.6 7.9 8.3 7.9 8.3 8.3 8.6 8.3 5.8 ...
## $ windspeed : num 6.7 4.3 8.5 8.4 1.1 3.8 0.4 2.5 0 0 ...
## $ winddir : num 5 40 322 329 356 45 0 270 0 0 ...
## $ sealevelpressure: num 1011 1011 1010 1010 1010 ...
## $ cloudcover : num 27.5 27.5 57.3 27.5 27.5 57.3 27.5 30.1 58.7 30.1 ...
## $ visibility : num 10 10 10 10 8.2 6.5 8.2 7.2 10 8.2 ...
## $ solarradiation : num 0 0 219 0 0 ...
## $ uvindex : num 0 0 2.18 0 0 ...
## $ severerisk : num 3 10 10 10 3 3 3 3 5 10 ...
## NULL2.12 Feature selection
Correlation check for non categorical and plot heatmap
2.13 Drop high correlation column
The main purpose of removing highly correlated variables is to avoid multicollinearity. Therefore, in order to improve the interpretability, stability and performance of the model, we choose to delete highly correlated variables. During the feature selection process, selecting to retain features that have a high correlation with the target variable but a low correlation between independent variables helps to build a more reliable model.
final_df_with_datetime = subset(dataframe, select = -c(feelslike, uvindex, humidity))
final_df = subset(final_df_with_datetime, select =-c(datetime))
print(colnames(final_df))## [1] "temp" "dew" "precip" "precipprob"
## [5] "preciptype" "windgust" "windspeed" "winddir"
## [9] "sealevelpressure" "cloudcover" "visibility" "solarradiation"
## [13] "severerisk"3 Modeling
3.1 Data Split
train_idx = createDataPartition(final_df$preciptype, p = 0.6, list =FALSE)
train_data = final_df[train_idx,]
test_data = final_df[-train_idx,]3.2 Random Forest Classifier
3.2.1 Train the random forest classifier
classifier_RF = randomForest(x = subset(train_data, select = -c(preciptype)), y =
as.factor(train_data$preciptype), ntree = 100, importance = TRUE)3.2.2 Make predictions on the test data
preds = predict(classifier_RF,
newdata = subset(test_data, select = -c(preciptype)))3.2.3 Calculate the confusion matrix/ accuracy of the predictions
conf_mat = table(preds, test_data$preciptype)
accuracy = sum(diag(conf_mat)) / sum(conf_mat)
print(conf_mat)##
## preds no rain rain
## no rain 2912 529
## rain 84 276print(paste("Accuracy:", round(accuracy, 3)))## [1] "Accuracy: 0.839"3.2.4 Explanation ：Confusion matrix:
- True Positives (TP): 276 (Actual Rain, Predicted Rain)
- True Negatives (TN): 2912 (Actual No Rain, Predicted No Rain)
- False Positives (FP): 529 (Actual No Rain, Predicted Rain)
- False Negatives (FN): 84 (Actual Rain, Predicted No Rain)Accuracy: The accuracy of the model is calculated as the sum of correctly predicted instances (TP + TN) divided by the total number of instances. In this case, the accuracy is approximately 83.9%.
Evaluation: The Random Forest Classifier shows good predictive performance with an accuracy of 83.9%. It effectively identifies both rainy and non-rainy instances. However, it is important to consider other metrics like precision, recall, and F1 score, especially when dealing with imbalanced datasets. These additional metrics provide a more comprehensive understanding of the classifier’s performance, especially in scenarios where certain classes may be underrepresented.
3.2.5 Feature important
imp = importance(classifier_RF)
print(imp)## no rain rain MeanDecreaseAccuracy MeanDecreaseGini
## temp 21.184492 -5.2786404 21.328875 209.47389
## dew 11.521755 1.6630562 11.697738 151.41772
## precip 11.037627 11.2320668 11.327388 131.03971
## precipprob 10.597874 11.1281148 11.097651 126.45147
## windgust 16.327441 -2.2202407 14.575924 189.91340
## windspeed 17.989084 -2.1688541 18.330626 181.60765
## winddir 12.507642 -3.0754240 12.590580 187.48759
## sealevelpressure 14.479662 2.2323244 16.010635 169.94403
## cloudcover 14.491565 10.6964784 19.083582 137.59800
## visibility 13.059319 0.5077438 12.738846 124.88253
## solarradiation 9.552764 10.5724896 15.023373 168.75532
## severerisk -1.671554 14.8430953 6.914683 63.56097varImpPlot(classifier_RF)
3.2.6 Classification
3.2.7 After feature important done
final_df = subset(final_df_with_datetime, select = -c(datetime, severerisk))
# Split the data into training and testing sets
train_idx = createDataPartition(final_df$preciptype, p = 0.6, list = FALSE)
train_data = final_df[train_idx,]
test_data = final_df[-train_idx,]
#Train the random forest classifier
final_classifier_RF = randomForest(x = subset(train_data, select = -c(preciptype)),
y = as.factor(train_data$preciptype),
ntree = 100,
importance = TRUE)3.2.8 Make predictions on the test data
preds = predict(final_classifier_RF, newdata = subset(test_data, select = -c(preciptype)))3.2.9 Calculate the confusion matrix/ accuracy of the predictions
conf_mat = table(preds, test_data$preciptype)
accuracy = sum(diag(conf_mat)) / sum(conf_mat)
print(conf_mat)##
## preds no rain rain
## no rain 2936 580
## rain 60 225print(paste("Accuracy:", round(accuracy, 3)))## [1] "Accuracy: 0.832"3.2.10 Feature important
imp = importance(final_classifier_RF)
print(imp)## no rain rain MeanDecreaseAccuracy MeanDecreaseGini
## temp 21.813808 -7.1415612 20.884089 202.8152
## dew 9.258264 0.8261106 8.692028 153.9571
## precip 10.786371 10.6712386 10.990769 132.4252
## precipprob 11.655619 12.9390553 12.622638 152.3706
## windgust 20.900046 -2.4622275 19.095025 198.5777
## windspeed 12.654678 -0.5801180 12.844751 192.3298
## winddir 10.470843 1.3579682 11.792601 200.6277
## sealevelpressure 11.906290 5.7131713 12.682249 179.6651
## cloudcover 11.677696 14.6520897 16.931623 140.2088
## visibility 8.510918 2.0878259 9.435113 125.5307
## solarradiation 14.971913 6.7086063 18.108947 173.0272varImpPlot(final_classifier_RF)
3.3 Time Series
3.3.1 Time series prediction
final_df = subset(final_df_with_datetime, select = -c(severerisk))
names(final_df)## [1] "datetime" "temp" "dew" "precip"
## [5] "precipprob" "preciptype" "windgust" "windspeed"
## [9] "winddir" "sealevelpressure" "cloudcover" "visibility"
## [13] "solarradiation"final_df$datetime <- seq.POSIXt(from = as.POSIXct("2022-03-01 00:00:00"), length.out = 9504, by = "60 mins")
head(final_df) ## datetime temp dew precip precipprob preciptype windgust windspeed
## 1 2022-03-01 00:00:00 26.2 22.9 0 0 no rain 9.4 6.7
## 2 2022-03-01 01:00:00 26.1 23.9 0 0 no rain 8.6 4.3
## 3 2022-03-01 02:00:00 25.9 23.8 0 0 no rain 7.9 8.5
## 4 2022-03-01 03:00:00 26.1 23.8 0 0 no rain 8.3 8.4
## 5 2022-03-01 04:00:00 25.2 23.8 0 0 no rain 7.9 1.1
## 6 2022-03-01 05:00:00 24.9 23.7 0 0 no rain 8.3 3.8
## winddir sealevelpressure cloudcover visibility solarradiation
## 1 5 1010.9 27.5 10.0 0.0000
## 2 40 1010.9 27.5 10.0 0.0000
## 3 322 1009.9 57.3 10.0 219.3828
## 4 329 1009.9 27.5 10.0 0.0000
## 5 356 1010.0 27.5 8.2 0.0000
## 6 45 1009.9 57.3 6.5 0.00003.3.2 ts() is time series function
data_temp <- ts(final_df$temp, start = decimal_date(ymd_hms("2022-03-01 00:00:00 UTC")), frequency = 9504)
data_dew <- ts(final_df$dew, start = decimal_date(ymd_hms("2022-03-01 00:00:00 UTC")), frequency = 9504)
data_precip <- ts(final_df$precip, start = decimal_date(ymd_hms("2022-03-01 00:00:00 UTC")), frequency = 9504)
data_precipprob <- ts(final_df$precipprob, start = decimal_date(ymd_hms("2022-03-01 00:00:00 UTC")), frequency = 9504)
data_windgust <- ts(final_df$windgust, start = decimal_date(ymd_hms("2022-03-01 00:00:00 UTC")), frequency = 9504)
data_windspeed <- ts(final_df$windspeed, start = decimal_date(ymd_hms("2022-03-01 00:00:00 UTC")), frequency = 9504)
data_winddir <- ts(final_df$winddir, start = decimal_date(ymd_hms("2022-03-01 00:00:00 UTC")), frequency = 9504)
data_sealevelpressure <- ts(final_df$sealevelpressure, start = decimal_date(ymd_hms("2022-03-01 00:00:00 UTC")), frequency = 9504)
data_cloudcover <- ts(final_df$cloudcover, start = decimal_date(ymd_hms("2022-03-01 00:00:00 UTC")), frequency = 9504)
data_visibility <- ts(final_df$visibility, start = decimal_date(ymd_hms("2022-03-01 00:00:00 UTC")), frequency = 9504)
data_solarradiation <- ts(final_df$solarradiation, start = decimal_date(ymd_hms("2022-03-01 00:00:00 UTC")), frequency = 9504)3.3.3 Use auto.arima() to get optimal auto ARIMA Model
autoarimal_temp <- auto.arima(data_temp)
autoarimal_temp## Series: data_temp
## ARIMA(2,1,5)
##
## Coefficients:
## ar1 ar2 ma1 ma2 ma3 ma4 ma5
## 1.8458 -0.9129 -1.6411 0.5687 0.0522 0.0107 0.0239
## s.e. 0.0063 0.0062 0.0120 0.0204 0.0207 0.0202 0.0108
##
## sigma^2 = 0.7127: log likelihood = -11872.46
## AIC=23760.92 AICc=23760.93 BIC=23818.19autoarimal_dew <- auto.arima(data_dew)
autoarimal_precip <- auto.arima(data_precip)
autoarimal_precipprob <- auto.arima(data_precipprob)
autoarimal_windgust <- auto.arima(data_windgust)
autoarimal_windspeed <- auto.arima(data_windspeed)
autoarimal_winddir <- auto.arima(data_winddir)
autoarimal_sealevelpressure <- auto.arima(data_sealevelpressure)
autoarimal_cloudcover <- auto.arima(data_cloudcover)
autoarimal_visibility <- auto.arima(data_visibility)
autoarimal_solarradiation <- auto.arima(data_solarradiation)3.3.4 Show the forecast data for 1 Week each hour (7 Day x 24 Hours = 168)
predict_temp <- forecast(autoarimal_temp, h = 168)
predict_dew <- forecast(autoarimal_dew, h = 168)
predict_precip <- forecast(autoarimal_precip, h = 168)
predict_precipprob <- forecast(autoarimal_precipprob, h = 168)
predict_windgust <- forecast(autoarimal_windgust, h = 168)
predict_windspeed <- forecast(autoarimal_windspeed, h = 168)
predict_winddir <- forecast(autoarimal_winddir, h = 168)
predict_sealevelpressure <- forecast(autoarimal_sealevelpressure, h = 168)
predict_cloudcover <- forecast(autoarimal_cloudcover, h = 168)
predict_visibility <- forecast(autoarimal_visibility, h = 168)
predict_solarradiation <- forecast(autoarimal_solarradiation, h = 168)
options(max.print = 10000)
head(predict_temp)## $method
## [1] "ARIMA(2,1,5)"
##
## $model
## Series: data_temp
## ARIMA(2,1,5)
##
## Coefficients:
## ar1 ar2 ma1 ma2 ma3 ma4 ma5
## 1.8458 -0.9129 -1.6411 0.5687 0.0522 0.0107 0.0239
## s.e. 0.0063 0.0062 0.0120 0.0204 0.0207 0.0202 0.0108
##
## sigma^2 = 0.7127: log likelihood = -11872.46
## AIC=23760.92 AICc=23760.93 BIC=23818.19
##
## $level
## [1] 80 95
##
## $mean
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## [1] 26.26666 26.65242 27.11316 27.60420 28.08137 28.51383 28.87640 29.15081
## [9] 29.32629 29.39966 29.37489 29.26217 29.07675 28.83741 28.56492 28.28048
## [17] 28.00424 27.75406 27.54446 27.38601 27.28489 27.24291 27.25775 27.32345
## [25] 27.43118 27.57004 27.72798 27.89274 28.05266 28.19740 28.31856 28.41006
## [33] 28.46832 28.49233 28.48346 28.44516 28.38258 28.30202 28.21046 28.11502
## [41] 28.02245 27.93872 27.86868 27.81584 27.78227 27.76854 27.77384 27.79616
## [49] 27.83252 27.87926 27.93233 27.98761 28.04120 28.08964 28.13013 28.16064
## [57] 28.17998 28.18783 28.18467 28.17166 28.15053 28.12342 28.09266 28.06064
## [65] 28.02962 28.00159 27.97819 27.96057 27.94943 27.94494 27.94682 27.95441
## [73] 27.96668 27.98241 28.00024 28.01879 28.03674 28.05296 28.06649 28.07666
## [81] 28.08308 28.08565 28.08452 28.08010 28.07297 28.06385 28.05351 28.04277
## [89] 28.03237 28.02299 28.01517 28.00930 28.00560 28.00413 28.00480 28.00738
## [97] 28.01152 28.01681 28.02280 28.02903 28.03505 28.04047 28.04499 28.04838
## [105] 28.05051 28.05135 28.05095 28.04945 28.04705 28.04398 28.04050 28.03690
## [113] 28.03342 28.03028 28.02766 28.02571 28.02448 28.02400 28.02424 28.02511
## [121] 28.02651 28.02829 28.03030 28.03239 28.03441 28.03622 28.03773 28.03886
## [129] 28.03957 28.03984 28.03970 28.03919 28.03838 28.03735 28.03618 28.03497
## [137] 28.03381 28.03276 28.03188 28.03123 28.03082 28.03067 28.03075 28.03105
## [145] 28.03152 28.03212 28.03279 28.03349 28.03417 28.03478 28.03528 28.03566
## [153] 28.03589 28.03598 28.03593 28.03576 28.03549 28.03514 28.03475 28.03434
## [161] 28.03395 28.03360 28.03331 28.03309 28.03295 28.03290 28.03293 28.03303
##
## $lower
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## 80% 95%
## 2023.162 25.18478 24.61207
## 2023.162 24.95864 24.06200
## 2023.162 24.95379 23.81069
## 2023.162 25.10384 23.78023
## 2023.162 25.35236 23.90770
## 2023.162 25.64548 24.12707
## 2023.162 25.93592 24.37933
## 2023.162 26.18270 24.61148
## 2023.162 26.35329 24.77948
## 2023.163 26.42641 24.85247
## 2023.163 26.39412 24.81620
## 2023.163 26.26213 24.67400
## 2023.163 26.04772 24.44425
## 2023.163 25.77572 24.15495
## 2023.163 25.47373 23.83736
## 2023.163 25.16799 23.52033
## 2023.163 24.88036 23.22667
## 2023.163 24.62683 22.97137
## 2023.164 24.41722 22.76176
## 2023.164 24.25594 22.59898
## 2023.164 24.14334 22.48031
## 2023.164 24.07734 22.40158
## 2023.164 24.05460 22.35895
## 2023.164 24.07119 22.34954
## 2023.164 24.12252 22.37103
## 2023.164 24.20302 22.42063
## 2023.164 24.30573 22.49410
## 2023.164 24.42236 22.58524
## 2023.165 24.54358 22.68598
## 2023.165 24.65972 22.78699
## 2023.165 24.76163 22.87870
## 2023.165 24.84148 22.95239
## 2023.165 24.89358 23.00123
## 2023.165 24.91486 23.02107
## 2023.165 24.90505 23.01076
## 2023.165 24.86654 22.97213
## 2023.165 24.80392 22.90950
## 2023.166 24.72336 22.82894
## 2023.166 24.63179 22.73735
## 2023.166 24.53620 22.64169
## 2023.166 24.44304 22.54821
## 2023.166 24.35773 22.46207
## 2023.166 24.28446 22.38709
## 2023.166 24.22608 22.32578
## 2023.166 24.18418 22.27946
## 2023.166 24.15916 22.24846
## 2023.166 24.15042 22.23230
## 2023.167 24.15653 22.22982
## 2023.167 24.17531 22.23930
## 2023.167 24.20404 22.25849
## 2023.167 24.23959 22.28477
## 2023.167 24.27861 22.31519
## 2023.167 24.31774 22.34666
## 2023.167 24.35378 22.37614
## 2023.167 24.38398 22.40089
## 2023.167 24.40615 22.41864
## 2023.168 24.41883 22.42779
## 2023.168 24.42134 22.42748
## 2023.168 24.41382 22.41766
## 2023.168 24.39712 22.39900
## 2023.168 24.37267 22.37280
## 2023.168 24.34237 22.34080
## 2023.168 24.30832 22.30500
## 2023.168 24.27270 22.26748
## 2023.168 24.23759 22.23021
## 2023.168 24.20482 22.19492
## 2023.169 24.17587 22.16304
## 2023.169 24.15183 22.13560
## 2023.169 24.13336 22.11325
## 2023.169 24.12070 22.09627
## 2023.169 24.11369 22.08455
## 2023.169 24.11183 22.07769
## 2023.169 24.11433 22.07503
## 2023.169 24.12020 22.07567
## 2023.169 24.12829 22.07861
## 2023.170 24.13742 22.08275
## 2023.170 24.14643 22.08702
## 2023.170 24.15424 22.09038
## 2023.170 24.15996 22.09197
## 2023.170 24.16291 22.09109
## 2023.170 24.16264 22.08728
## 2023.170 24.15898 22.08033
## 2023.170 24.15199 22.07024
## 2023.170 24.14198 22.05726
## 2023.170 24.12941 22.04182
## 2023.171 24.11491 22.02446
## 2023.171 24.09915 22.00584
## 2023.171 24.08287 21.98663
## 2023.171 24.06675 21.96747
## 2023.171 24.05140 21.94896
## 2023.171 24.03733 21.93158
## 2023.171 24.02491 21.91571
## 2023.171 24.01439 21.90157
## 2023.171 24.00584 21.88927
## 2023.172 23.99922 21.87879
## 2023.172 23.99435 21.86999
## 2023.172 23.99097 21.86261
## 2023.172 23.98871 21.85636
## 2023.172 23.98720 21.85088
## 2023.172 23.98602 21.84578
## 2023.172 23.98477 21.84069
## 2023.172 23.98311 21.83528
## 2023.172 23.98073 21.82925
## 2023.172 23.97742 21.82238
## 2023.173 23.97302 21.81452
## 2023.173 23.96748 21.80561
## 2023.173 23.96084 21.79567
## 2023.173 23.95319 21.78476
## 2023.173 23.94470 21.77305
## 2023.173 23.93557 21.76071
## 2023.173 23.92603 21.74796
## 2023.173 23.91632 21.73502
## 2023.173 23.90668 21.72211
## 2023.174 23.89730 21.70943
## 2023.174 23.88836 21.69714
## 2023.174 23.87999 21.68537
## 2023.174 23.87226 21.67420
## 2023.174 23.86520 21.66367
## 2023.174 23.85881 21.65376
## 2023.174 23.85301 21.64443
## 2023.174 23.84771 21.63559
## 2023.174 23.84280 21.62713
## 2023.174 23.83813 21.61893
## 2023.175 23.83359 21.61087
## 2023.175 23.82902 21.60282
## 2023.175 23.82431 21.59466
## 2023.175 23.81936 21.58629
## 2023.175 23.81410 21.57764
## 2023.175 23.80848 21.56867
## 2023.175 23.80248 21.55936
## 2023.175 23.79612 21.54970
## 2023.175 23.78942 21.53973
## 2023.176 23.78245 21.52950
## 2023.176 23.77528 21.51907
## 2023.176 23.76797 21.50851
## 2023.176 23.76061 21.49790
## 2023.176 23.75328 21.48731
## 2023.176 23.74605 21.47681
## 2023.176 23.73898 21.46645
## 2023.176 23.73210 21.45627
## 2023.176 23.72544 21.44631
## 2023.176 23.71902 21.43657
## 2023.177 23.71282 21.42705
## 2023.177 23.70683 21.41773
## 2023.177 23.70102 21.40859
## 2023.177 23.69534 21.39958
## 2023.177 23.68974 21.39067
## 2023.177 23.68419 21.38181
## 2023.177 23.67864 21.37296
## 2023.177 23.67304 21.36408
## 2023.177 23.66737 21.35514
## 2023.178 23.66160 21.34611
## 2023.178 23.65571 21.33698
## 2023.178 23.64970 21.32774
## 2023.178 23.64357 21.31839
## 2023.178 23.63733 21.30895
## 2023.178 23.63101 21.29943
## 2023.178 23.62463 21.28984
## 2023.178 23.61821 21.28023
## 2023.178 23.61177 21.27060
## 2023.178 23.60535 21.26100
## 2023.179 23.59897 21.25143
## 2023.179 23.59265 21.24191
## 2023.179 23.58640 21.23247
## 2023.179 23.58023 21.22311
## 2023.179 23.57415 21.21383
## 2023.179 23.56814 21.20463
## 2023.179 23.56221 21.19550
##
## $upper
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## 80% 95%
## 2023.162 27.34854 27.92125
## 2023.162 28.34621 29.24284
## 2023.162 29.27252 30.41562
## 2023.162 30.10455 31.42816
## 2023.162 30.81039 32.25505
## 2023.162 31.38218 32.90059
## 2023.162 31.81688 33.37347
## 2023.162 32.11891 33.69013
## 2023.162 32.29928 33.87309
## 2023.163 32.37291 33.94685
## 2023.163 32.35565 33.93357
## 2023.163 32.26222 33.85035
## 2023.163 32.10578 33.70926
## 2023.163 31.89910 33.51986
## 2023.163 31.65611 33.29248
## 2023.163 31.39298 33.04063
## 2023.163 31.12813 32.78182
## 2023.163 30.88129 32.53674
## 2023.164 30.67170 32.32716
## 2023.164 30.51607 32.17303
## 2023.164 30.42643 32.08947
## 2023.164 30.40849 32.08424
## 2023.164 30.46089 32.15654
## 2023.164 30.57571 32.29735
## 2023.164 30.73983 32.49132
## 2023.164 30.93705 32.71944
## 2023.164 31.15023 32.96187
## 2023.164 31.36313 33.20024
## 2023.165 31.56174 33.41933
## 2023.165 31.73507 33.60780
## 2023.165 31.87549 33.75842
## 2023.165 31.97863 33.86772
## 2023.165 32.04306 33.93541
## 2023.165 32.06980 33.96360
## 2023.165 32.06187 33.95617
## 2023.165 32.02379 33.91820
## 2023.165 31.96123 33.85565
## 2023.166 31.88067 33.77510
## 2023.166 31.78913 33.68357
## 2023.166 31.69384 33.58836
## 2023.166 31.60187 33.49669
## 2023.166 31.51971 33.41537
## 2023.166 31.45290 33.35027
## 2023.166 31.40561 33.30591
## 2023.166 31.38037 33.28508
## 2023.166 31.37792 33.28861
## 2023.166 31.39725 33.31537
## 2023.167 31.43579 33.36250
## 2023.167 31.48974 33.42575
## 2023.167 31.55448 33.50003
## 2023.167 31.62506 33.57988
## 2023.167 31.69661 33.66003
## 2023.167 31.76466 33.73574
## 2023.167 31.82550 33.80314
## 2023.167 31.87628 33.85937
## 2023.167 31.91512 33.90263
## 2023.168 31.94114 33.93217
## 2023.168 31.95433 33.94819
## 2023.168 31.95552 33.95168
## 2023.168 31.94620 33.94432
## 2023.168 31.92839 33.92827
## 2023.168 31.90447 33.90604
## 2023.168 31.87700 33.88032
## 2023.168 31.84858 33.85379
## 2023.168 31.82164 33.82902
## 2023.168 31.79837 33.80826
## 2023.169 31.78051 33.79333
## 2023.169 31.76932 33.78554
## 2023.169 31.76549 33.78560
## 2023.169 31.76917 33.79360
## 2023.169 31.77996 33.80910
## 2023.169 31.79698 33.83112
## 2023.169 31.81902 33.85833
## 2023.169 31.84462 33.88915
## 2023.169 31.87218 33.92187
## 2023.170 31.90015 33.95482
## 2023.170 31.92706 33.98647
## 2023.170 31.95168 34.01553
## 2023.170 31.97301 34.04100
## 2023.170 31.99041 34.06222
## 2023.170 32.00352 34.07887
## 2023.170 32.01231 34.09096
## 2023.170 32.01705 34.09880
## 2023.170 32.01823 34.10294
## 2023.170 32.01653 34.10413
## 2023.171 32.01279 34.10323
## 2023.171 32.00787 34.10118
## 2023.171 32.00266 34.09891
## 2023.171 31.99800 34.09727
## 2023.171 31.99459 34.09703
## 2023.171 31.99302 34.09876
## 2023.171 31.99369 34.10290
## 2023.171 31.99681 34.10963
## 2023.171 32.00243 34.11899
## 2023.172 32.01039 34.13081
## 2023.172 32.02040 34.14477
## 2023.172 32.03207 34.16043
## 2023.172 32.04492 34.17727
## 2023.172 32.05841 34.19473
## 2023.172 32.07204 34.21228
## 2023.172 32.08532 34.22940
## 2023.172 32.09783 34.24566
## 2023.172 32.10925 34.26074
## 2023.172 32.11935 34.27439
## 2023.173 32.12801 34.28651
## 2023.173 32.13522 34.29709
## 2023.173 32.14107 34.30624
## 2023.173 32.14571 34.31414
## 2023.173 32.14939 34.32105
## 2023.173 32.15238 34.32724
## 2023.173 32.15498 34.33305
## 2023.173 32.15748 34.33878
## 2023.173 32.16016 34.34472
## 2023.174 32.16326 34.35113
## 2023.174 32.16697 34.35819
## 2023.174 32.17143 34.36604
## 2023.174 32.17670 34.37475
## 2023.174 32.18279 34.38433
## 2023.174 32.18967 34.39471
## 2023.174 32.19722 34.40579
## 2023.174 32.20531 34.41743
## 2023.174 32.21378 34.42945
## 2023.174 32.22247 34.44167
## 2023.175 32.23120 34.45391
## 2023.175 32.23980 34.46600
## 2023.175 32.24814 34.47779
## 2023.175 32.25611 34.48918
## 2023.175 32.26363 34.50008
## 2023.175 32.27066 34.51047
## 2023.175 32.27721 34.52033
## 2023.175 32.28329 34.52971
## 2023.175 32.28896 34.53866
## 2023.176 32.29431 34.54726
## 2023.176 32.29942 34.55563
## 2023.176 32.30440 34.56385
## 2023.176 32.30933 34.57204
## 2023.176 32.31433 34.58030
## 2023.176 32.31946 34.58870
## 2023.176 32.32479 34.59732
## 2023.176 32.33036 34.60619
## 2023.176 32.33620 34.61533
## 2023.176 32.34231 34.62476
## 2023.177 32.34868 34.63445
## 2023.177 32.35526 34.64436
## 2023.177 32.36202 34.65445
## 2023.177 32.36890 34.66465
## 2023.177 32.37584 34.67492
## 2023.177 32.38280 34.68518
## 2023.177 32.38970 34.69538
## 2023.177 32.39651 34.70548
## 2023.177 32.40319 34.71543
## 2023.178 32.40972 34.72521
## 2023.178 32.41608 34.73481
## 2023.178 32.42227 34.74423
## 2023.178 32.42830 34.75348
## 2023.178 32.43419 34.76257
## 2023.178 32.43996 34.77155
## 2023.178 32.44565 34.78043
## 2023.178 32.45129 34.78926
## 2023.178 32.45691 34.79808
## 2023.178 32.46255 34.80690
## 2023.179 32.46822 34.81577
## 2023.179 32.47396 34.82470
## 2023.179 32.47978 34.83371
## 2023.179 32.48567 34.84280
## 2023.179 32.49166 34.85198
## 2023.179 32.49772 34.86124
## 2023.179 32.50385 34.87056head(predict_dew)## $method
## [1] "ARIMA(2,1,1)"
##
## $model
## Series: data_dew
## ARIMA(2,1,1)
##
## Coefficients:
## ar1 ar2 ma1
## 0.6698 0.1028 -0.9795
## s.e. 0.0112 0.0110 0.0043
##
## sigma^2 = 0.3286: log likelihood = -8195.39
## AIC=16398.79 AICc=16398.79 BIC=16427.42
##
## $level
## [1] 80 95
##
## $mean
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## [1] 24.57571 24.47688 24.38762 24.31768 24.26165 24.21694 24.18123 24.15271
## [9] 24.12994 24.11175 24.09723 24.08564 24.07638 24.06898 24.06308 24.05836
## [17] 24.05460 24.05159 24.04919 24.04727 24.04574 24.04452 24.04354 24.04276
## [25] 24.04214 24.04164 24.04125 24.04093 24.04068 24.04047 24.04031 24.04018
## [33] 24.04008 24.04000 24.03993 24.03988 24.03984 24.03981 24.03978 24.03976
## [41] 24.03974 24.03973 24.03972 24.03971 24.03970 24.03969 24.03969 24.03969
## [49] 24.03968 24.03968 24.03968 24.03968 24.03968 24.03968 24.03968 24.03967
## [57] 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967
## [65] 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967
## [73] 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967
## [81] 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967
## [89] 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967
## [97] 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967
## [105] 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967
## [113] 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967
## [121] 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967
## [129] 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967
## [137] 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967
## [145] 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967
## [153] 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967
## [161] 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967 24.03967
##
## $lower
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## 80% 95%
## 2023.162 23.84106 23.45217
## 2023.162 23.58421 23.11166
## 2023.162 23.39669 22.87213
## 2023.162 23.26497 22.70769
## 2023.162 23.16777 22.58871
## 2023.162 23.09470 22.50062
## 2023.162 23.03886 22.43413
## 2023.162 22.99565 22.38314
## 2023.162 22.96185 22.34350
## 2023.163 22.93517 22.31232
## 2023.163 22.91391 22.28750
## 2023.163 22.89684 22.26753
## 2023.163 22.88301 22.25128
## 2023.163 22.87171 22.23791
## 2023.163 22.86239 22.22678
## 2023.163 22.85463 22.21741
## 2023.163 22.84809 22.20941
## 2023.163 22.84253 22.20250
## 2023.164 22.83774 22.19644
## 2023.164 22.83357 22.19107
## 2023.164 22.82987 22.18623
## 2023.164 22.82657 22.18183
## 2023.164 22.82357 22.17776
## 2023.164 22.82083 22.17397
## 2023.164 22.81827 22.17040
## 2023.164 22.81588 22.16700
## 2023.164 22.81361 22.16374
## 2023.164 22.81144 22.16059
## 2023.165 22.80935 22.15753
## 2023.165 22.80733 22.15455
## 2023.165 22.80536 22.15162
## 2023.165 22.80344 22.14874
## 2023.165 22.80154 22.14590
## 2023.165 22.79968 22.14309
## 2023.165 22.79784 22.14031
## 2023.165 22.79601 22.13755
## 2023.165 22.79421 22.13481
## 2023.166 22.79241 22.13208
## 2023.166 22.79063 22.12937
## 2023.166 22.78885 22.12666
## 2023.166 22.78709 22.12397
## 2023.166 22.78532 22.12128
## 2023.166 22.78357 22.11861
## 2023.166 22.78182 22.11593
## 2023.166 22.78007 22.11327
## 2023.166 22.77833 22.11061
## 2023.166 22.77659 22.10795
## 2023.167 22.77486 22.10530
## 2023.167 22.77313 22.10266
## 2023.167 22.77140 22.10002
## 2023.167 22.76968 22.09738
## 2023.167 22.76795 22.09474
## 2023.167 22.76623 22.09212
## 2023.167 22.76452 22.08949
## 2023.167 22.76280 22.08687
## 2023.167 22.76109 22.08425
## 2023.168 22.75938 22.08164
## 2023.168 22.75767 22.07903
## 2023.168 22.75597 22.07642
## 2023.168 22.75427 22.07381
## 2023.168 22.75257 22.07121
## 2023.168 22.75087 22.06862
## 2023.168 22.74917 22.06602
## 2023.168 22.74748 22.06344
## 2023.168 22.74579 22.06085
## 2023.168 22.74410 22.05827
## 2023.169 22.74241 22.05569
## 2023.169 22.74073 22.05311
## 2023.169 22.73905 22.05054
## 2023.169 22.73737 22.04797
## 2023.169 22.73569 22.04540
## 2023.169 22.73401 22.04284
## 2023.169 22.73234 22.04028
## 2023.169 22.73067 22.03772
## 2023.169 22.72900 22.03517
## 2023.170 22.72733 22.03262
## 2023.170 22.72567 22.03007
## 2023.170 22.72400 22.02753
## 2023.170 22.72234 22.02499
## 2023.170 22.72068 22.02245
## 2023.170 22.71903 22.01992
## 2023.170 22.71737 22.01739
## 2023.170 22.71572 22.01486
## 2023.170 22.71407 22.01234
## 2023.170 22.71242 22.00982
## 2023.171 22.71077 22.00730
## 2023.171 22.70913 22.00478
## 2023.171 22.70749 22.00227
## 2023.171 22.70585 21.99976
## 2023.171 22.70421 21.99726
## 2023.171 22.70257 21.99476
## 2023.171 22.70094 21.99226
## 2023.171 22.69931 21.98976
## 2023.171 22.69768 21.98727
## 2023.172 22.69605 21.98478
## 2023.172 22.69442 21.98229
## 2023.172 22.69280 21.97980
## 2023.172 22.69117 21.97732
## 2023.172 22.68955 21.97484
## 2023.172 22.68794 21.97237
## 2023.172 22.68632 21.96990
## 2023.172 22.68470 21.96743
## 2023.172 22.68309 21.96496
## 2023.172 22.68148 21.96250
## 2023.173 22.67987 21.96004
## 2023.173 22.67826 21.95758
## 2023.173 22.67666 21.95512
## 2023.173 22.67506 21.95267
## 2023.173 22.67345 21.95022
## 2023.173 22.67186 21.94778
## 2023.173 22.67026 21.94533
## 2023.173 22.66866 21.94289
## 2023.173 22.66707 21.94045
## 2023.174 22.66548 21.93802
## 2023.174 22.66389 21.93559
## 2023.174 22.66230 21.93316
## 2023.174 22.66071 21.93073
## 2023.174 22.65913 21.92831
## 2023.174 22.65754 21.92589
## 2023.174 22.65596 21.92347
## 2023.174 22.65438 21.92105
## 2023.174 22.65280 21.91864
## 2023.174 22.65123 21.91623
## 2023.175 22.64966 21.91382
## 2023.175 22.64808 21.91142
## 2023.175 22.64651 21.90902
## 2023.175 22.64494 21.90662
## 2023.175 22.64338 21.90422
## 2023.175 22.64181 21.90183
## 2023.175 22.64025 21.89944
## 2023.175 22.63869 21.89705
## 2023.175 22.63713 21.89466
## 2023.176 22.63557 21.89228
## 2023.176 22.63401 21.88990
## 2023.176 22.63246 21.88752
## 2023.176 22.63091 21.88515
## 2023.176 22.62935 21.88278
## 2023.176 22.62780 21.88041
## 2023.176 22.62626 21.87804
## 2023.176 22.62471 21.87567
## 2023.176 22.62317 21.87331
## 2023.176 22.62162 21.87095
## 2023.177 22.62008 21.86860
## 2023.177 22.61854 21.86624
## 2023.177 22.61700 21.86389
## 2023.177 22.61547 21.86154
## 2023.177 22.61393 21.85919
## 2023.177 22.61240 21.85685
## 2023.177 22.61087 21.85451
## 2023.177 22.60934 21.85217
## 2023.177 22.60781 21.84983
## 2023.178 22.60629 21.84750
## 2023.178 22.60476 21.84517
## 2023.178 22.60324 21.84284
## 2023.178 22.60172 21.84051
## 2023.178 22.60020 21.83819
## 2023.178 22.59868 21.83586
## 2023.178 22.59716 21.83354
## 2023.178 22.59565 21.83123
## 2023.178 22.59413 21.82891
## 2023.178 22.59262 21.82660
## 2023.179 22.59111 21.82429
## 2023.179 22.58960 21.82198
## 2023.179 22.58810 21.81968
## 2023.179 22.58659 21.81738
## 2023.179 22.58509 21.81508
## 2023.179 22.58358 21.81278
## 2023.179 22.58208 21.81048
##
## $upper
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## 80% 95%
## 2023.162 25.31035 25.69924
## 2023.162 25.36954 25.84209
## 2023.162 25.37855 25.90312
## 2023.162 25.37039 25.92766
## 2023.162 25.35554 25.93460
## 2023.162 25.33918 25.93325
## 2023.162 25.32359 25.92833
## 2023.162 25.30977 25.92228
## 2023.162 25.29803 25.91637
## 2023.163 25.28834 25.91119
## 2023.163 25.28055 25.90697
## 2023.163 25.27444 25.90375
## 2023.163 25.26975 25.90148
## 2023.163 25.26626 25.90006
## 2023.163 25.26377 25.89938
## 2023.163 25.26210 25.89932
## 2023.163 25.26110 25.89979
## 2023.163 25.26065 25.90069
## 2023.164 25.26064 25.90194
## 2023.164 25.26098 25.90348
## 2023.164 25.26161 25.90525
## 2023.164 25.26247 25.90721
## 2023.164 25.26351 25.90932
## 2023.164 25.26470 25.91156
## 2023.164 25.26601 25.91388
## 2023.164 25.26741 25.91629
## 2023.164 25.26888 25.91876
## 2023.164 25.27042 25.92127
## 2023.165 25.27200 25.92382
## 2023.165 25.27362 25.92640
## 2023.165 25.27526 25.92900
## 2023.165 25.27693 25.93162
## 2023.165 25.27862 25.93426
## 2023.165 25.28032 25.93690
## 2023.165 25.28203 25.93955
## 2023.165 25.28375 25.94221
## 2023.165 25.28547 25.94487
## 2023.166 25.28720 25.94753
## 2023.166 25.28893 25.95019
## 2023.166 25.29066 25.95285
## 2023.166 25.29239 25.95551
## 2023.166 25.29413 25.95817
## 2023.166 25.29586 25.96083
## 2023.166 25.29759 25.96348
## 2023.166 25.29933 25.96613
## 2023.166 25.30106 25.96878
## 2023.166 25.30279 25.97143
## 2023.167 25.30451 25.97407
## 2023.167 25.30624 25.97671
## 2023.167 25.30796 25.97935
## 2023.167 25.30968 25.98198
## 2023.167 25.31140 25.98461
## 2023.167 25.31312 25.98724
## 2023.167 25.31483 25.98986
## 2023.167 25.31655 25.99248
## 2023.167 25.31826 25.99510
## 2023.168 25.31997 25.99771
## 2023.168 25.32167 26.00032
## 2023.168 25.32338 26.00293
## 2023.168 25.32508 26.00553
## 2023.168 25.32678 26.00813
## 2023.168 25.32848 26.01073
## 2023.168 25.33017 26.01332
## 2023.168 25.33187 26.01591
## 2023.168 25.33356 26.01850
## 2023.168 25.33525 26.02108
## 2023.169 25.33693 26.02366
## 2023.169 25.33862 26.02624
## 2023.169 25.34030 26.02881
## 2023.169 25.34198 26.03138
## 2023.169 25.34366 26.03394
## 2023.169 25.34533 26.03651
## 2023.169 25.34701 26.03907
## 2023.169 25.34868 26.04162
## 2023.169 25.35035 26.04418
## 2023.170 25.35201 26.04673
## 2023.170 25.35368 26.04927
## 2023.170 25.35534 26.05181
## 2023.170 25.35700 26.05436
## 2023.170 25.35866 26.05689
## 2023.170 25.36032 26.05943
## 2023.170 25.36197 26.06196
## 2023.170 25.36363 26.06448
## 2023.170 25.36528 26.06701
## 2023.170 25.36692 26.06953
## 2023.171 25.36857 26.07205
## 2023.171 25.37022 26.07456
## 2023.171 25.37186 26.07707
## 2023.171 25.37350 26.07958
## 2023.171 25.37514 26.08209
## 2023.171 25.37677 26.08459
## 2023.171 25.37841 26.08709
## 2023.171 25.38004 26.08959
## 2023.171 25.38167 26.09208
## 2023.172 25.38330 26.09457
## 2023.172 25.38492 26.09706
## 2023.172 25.38655 26.09954
## 2023.172 25.38817 26.10202
## 2023.172 25.38979 26.10450
## 2023.172 25.39141 26.10698
## 2023.172 25.39303 26.10945
## 2023.172 25.39464 26.11192
## 2023.172 25.39625 26.11438
## 2023.172 25.39786 26.11685
## 2023.173 25.39947 26.11931
## 2023.173 25.40108 26.12177
## 2023.173 25.40269 26.12422
## 2023.173 25.40429 26.12667
## 2023.173 25.40589 26.12912
## 2023.173 25.40749 26.13157
## 2023.173 25.40909 26.13401
## 2023.173 25.41068 26.13645
## 2023.173 25.41228 26.13889
## 2023.174 25.41387 26.14133
## 2023.174 25.41546 26.14376
## 2023.174 25.41705 26.14619
## 2023.174 25.41863 26.14861
## 2023.174 25.42022 26.15104
## 2023.174 25.42180 26.15346
## 2023.174 25.42338 26.15588
## 2023.174 25.42496 26.15829
## 2023.174 25.42654 26.16070
## 2023.174 25.42812 26.16311
## 2023.175 25.42969 26.16552
## 2023.175 25.43126 26.16793
## 2023.175 25.43283 26.17033
## 2023.175 25.43440 26.17273
## 2023.175 25.43597 26.17512
## 2023.175 25.43753 26.17752
## 2023.175 25.43910 26.17991
## 2023.175 25.44066 26.18229
## 2023.175 25.44222 26.18468
## 2023.176 25.44378 26.18706
## 2023.176 25.44533 26.18944
## 2023.176 25.44689 26.19182
## 2023.176 25.44844 26.19420
## 2023.176 25.44999 26.19657
## 2023.176 25.45154 26.19894
## 2023.176 25.45309 26.20131
## 2023.176 25.45463 26.20367
## 2023.176 25.45618 26.20603
## 2023.176 25.45772 26.20839
## 2023.177 25.45926 26.21075
## 2023.177 25.46080 26.21310
## 2023.177 25.46234 26.21546
## 2023.177 25.46388 26.21780
## 2023.177 25.46541 26.22015
## 2023.177 25.46694 26.22250
## 2023.177 25.46847 26.22484
## 2023.177 25.47000 26.22718
## 2023.177 25.47153 26.22951
## 2023.178 25.47306 26.23185
## 2023.178 25.47458 26.23418
## 2023.178 25.47611 26.23651
## 2023.178 25.47763 26.23884
## 2023.178 25.47915 26.24116
## 2023.178 25.48067 26.24348
## 2023.178 25.48218 26.24580
## 2023.178 25.48370 26.24812
## 2023.178 25.48521 26.25043
## 2023.178 25.48672 26.25274
## 2023.179 25.48823 26.25505
## 2023.179 25.48974 26.25736
## 2023.179 25.49125 26.25967
## 2023.179 25.49275 26.26197
## 2023.179 25.49426 26.26427
## 2023.179 25.49576 26.26657
## 2023.179 25.49726 26.26886head(predict_precip)## $method
## [1] "ARIMA(0,0,0) with non-zero mean"
##
## $model
## Series: data_precip
## ARIMA(0,0,0) with non-zero mean
##
## Coefficients:
## mean
## 0.2727
## s.e. 0.0273
##
## sigma^2 = 7.101: log likelihood = -22800.29
## AIC=45604.59 AICc=45604.59 BIC=45618.91
##
## $level
## [1] 80 95
##
## $mean
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## [1] 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559
## [8] 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559
## [15] 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559
## [22] 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559
## [29] 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559
## [36] 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559
## [43] 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559
## [50] 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559
## [57] 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559
## [64] 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559
## [71] 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559
## [78] 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559
## [85] 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559
## [92] 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559
## [99] 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559
## [106] 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559
## [113] 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559
## [120] 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559
## [127] 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559
## [134] 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559
## [141] 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559
## [148] 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559
## [155] 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559
## [162] 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559 0.2726559
##
## $lower
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## 80% 95%
## 2023.162 -3.142443 -4.950287
## 2023.162 -3.142443 -4.950287
## 2023.162 -3.142443 -4.950287
## 2023.162 -3.142443 -4.950287
## 2023.162 -3.142443 -4.950287
## 2023.162 -3.142443 -4.950287
## 2023.162 -3.142443 -4.950287
## 2023.162 -3.142443 -4.950287
## 2023.162 -3.142443 -4.950287
## 2023.163 -3.142443 -4.950287
## 2023.163 -3.142443 -4.950287
## 2023.163 -3.142443 -4.950287
## 2023.163 -3.142443 -4.950287
## 2023.163 -3.142443 -4.950287
## 2023.163 -3.142443 -4.950287
## 2023.163 -3.142443 -4.950287
## 2023.163 -3.142443 -4.950287
## 2023.163 -3.142443 -4.950287
## 2023.164 -3.142443 -4.950287
## 2023.164 -3.142443 -4.950287
## 2023.164 -3.142443 -4.950287
## 2023.164 -3.142443 -4.950287
## 2023.164 -3.142443 -4.950287
## 2023.164 -3.142443 -4.950287
## 2023.164 -3.142443 -4.950287
## 2023.164 -3.142443 -4.950287
## 2023.164 -3.142443 -4.950287
## 2023.164 -3.142443 -4.950287
## 2023.165 -3.142443 -4.950287
## 2023.165 -3.142443 -4.950287
## 2023.165 -3.142443 -4.950287
## 2023.165 -3.142443 -4.950287
## 2023.165 -3.142443 -4.950287
## 2023.165 -3.142443 -4.950287
## 2023.165 -3.142443 -4.950287
## 2023.165 -3.142443 -4.950287
## 2023.165 -3.142443 -4.950287
## 2023.166 -3.142443 -4.950287
## 2023.166 -3.142443 -4.950287
## 2023.166 -3.142443 -4.950287
## 2023.166 -3.142443 -4.950287
## 2023.166 -3.142443 -4.950287
## 2023.166 -3.142443 -4.950287
## 2023.166 -3.142443 -4.950287
## 2023.166 -3.142443 -4.950287
## 2023.166 -3.142443 -4.950287
## 2023.166 -3.142443 -4.950287
## 2023.167 -3.142443 -4.950287
## 2023.167 -3.142443 -4.950287
## 2023.167 -3.142443 -4.950287
## 2023.167 -3.142443 -4.950287
## 2023.167 -3.142443 -4.950287
## 2023.167 -3.142443 -4.950287
## 2023.167 -3.142443 -4.950287
## 2023.167 -3.142443 -4.950287
## 2023.167 -3.142443 -4.950287
## 2023.168 -3.142443 -4.950287
## 2023.168 -3.142443 -4.950287
## 2023.168 -3.142443 -4.950287
## 2023.168 -3.142443 -4.950287
## 2023.168 -3.142443 -4.950287
## 2023.168 -3.142443 -4.950287
## 2023.168 -3.142443 -4.950287
## 2023.168 -3.142443 -4.950287
## 2023.168 -3.142443 -4.950287
## 2023.168 -3.142443 -4.950287
## 2023.169 -3.142443 -4.950287
## 2023.169 -3.142443 -4.950287
## 2023.169 -3.142443 -4.950287
## 2023.169 -3.142443 -4.950287
## 2023.169 -3.142443 -4.950287
## 2023.169 -3.142443 -4.950287
## 2023.169 -3.142443 -4.950287
## 2023.169 -3.142443 -4.950287
## 2023.169 -3.142443 -4.950287
## 2023.170 -3.142443 -4.950287
## 2023.170 -3.142443 -4.950287
## 2023.170 -3.142443 -4.950287
## 2023.170 -3.142443 -4.950287
## 2023.170 -3.142443 -4.950287
## 2023.170 -3.142443 -4.950287
## 2023.170 -3.142443 -4.950287
## 2023.170 -3.142443 -4.950287
## 2023.170 -3.142443 -4.950287
## 2023.170 -3.142443 -4.950287
## 2023.171 -3.142443 -4.950287
## 2023.171 -3.142443 -4.950287
## 2023.171 -3.142443 -4.950287
## 2023.171 -3.142443 -4.950287
## 2023.171 -3.142443 -4.950287
## 2023.171 -3.142443 -4.950287
## 2023.171 -3.142443 -4.950287
## 2023.171 -3.142443 -4.950287
## 2023.171 -3.142443 -4.950287
## 2023.172 -3.142443 -4.950287
## 2023.172 -3.142443 -4.950287
## 2023.172 -3.142443 -4.950287
## 2023.172 -3.142443 -4.950287
## 2023.172 -3.142443 -4.950287
## 2023.172 -3.142443 -4.950287
## 2023.172 -3.142443 -4.950287
## 2023.172 -3.142443 -4.950287
## 2023.172 -3.142443 -4.950287
## 2023.172 -3.142443 -4.950287
## 2023.173 -3.142443 -4.950287
## 2023.173 -3.142443 -4.950287
## 2023.173 -3.142443 -4.950287
## 2023.173 -3.142443 -4.950287
## 2023.173 -3.142443 -4.950287
## 2023.173 -3.142443 -4.950287
## 2023.173 -3.142443 -4.950287
## 2023.173 -3.142443 -4.950287
## 2023.173 -3.142443 -4.950287
## 2023.174 -3.142443 -4.950287
## 2023.174 -3.142443 -4.950287
## 2023.174 -3.142443 -4.950287
## 2023.174 -3.142443 -4.950287
## 2023.174 -3.142443 -4.950287
## 2023.174 -3.142443 -4.950287
## 2023.174 -3.142443 -4.950287
## 2023.174 -3.142443 -4.950287
## 2023.174 -3.142443 -4.950287
## 2023.174 -3.142443 -4.950287
## 2023.175 -3.142443 -4.950287
## 2023.175 -3.142443 -4.950287
## 2023.175 -3.142443 -4.950287
## 2023.175 -3.142443 -4.950287
## 2023.175 -3.142443 -4.950287
## 2023.175 -3.142443 -4.950287
## 2023.175 -3.142443 -4.950287
## 2023.175 -3.142443 -4.950287
## 2023.175 -3.142443 -4.950287
## 2023.176 -3.142443 -4.950287
## 2023.176 -3.142443 -4.950287
## 2023.176 -3.142443 -4.950287
## 2023.176 -3.142443 -4.950287
## 2023.176 -3.142443 -4.950287
## 2023.176 -3.142443 -4.950287
## 2023.176 -3.142443 -4.950287
## 2023.176 -3.142443 -4.950287
## 2023.176 -3.142443 -4.950287
## 2023.176 -3.142443 -4.950287
## 2023.177 -3.142443 -4.950287
## 2023.177 -3.142443 -4.950287
## 2023.177 -3.142443 -4.950287
## 2023.177 -3.142443 -4.950287
## 2023.177 -3.142443 -4.950287
## 2023.177 -3.142443 -4.950287
## 2023.177 -3.142443 -4.950287
## 2023.177 -3.142443 -4.950287
## 2023.177 -3.142443 -4.950287
## 2023.178 -3.142443 -4.950287
## 2023.178 -3.142443 -4.950287
## 2023.178 -3.142443 -4.950287
## 2023.178 -3.142443 -4.950287
## 2023.178 -3.142443 -4.950287
## 2023.178 -3.142443 -4.950287
## 2023.178 -3.142443 -4.950287
## 2023.178 -3.142443 -4.950287
## 2023.178 -3.142443 -4.950287
## 2023.178 -3.142443 -4.950287
## 2023.179 -3.142443 -4.950287
## 2023.179 -3.142443 -4.950287
## 2023.179 -3.142443 -4.950287
## 2023.179 -3.142443 -4.950287
## 2023.179 -3.142443 -4.950287
## 2023.179 -3.142443 -4.950287
## 2023.179 -3.142443 -4.950287
##
## $upper
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## 80% 95%
## 2023.162 3.687755 5.495599
## 2023.162 3.687755 5.495599
## 2023.162 3.687755 5.495599
## 2023.162 3.687755 5.495599
## 2023.162 3.687755 5.495599
## 2023.162 3.687755 5.495599
## 2023.162 3.687755 5.495599
## 2023.162 3.687755 5.495599
## 2023.162 3.687755 5.495599
## 2023.163 3.687755 5.495599
## 2023.163 3.687755 5.495599
## 2023.163 3.687755 5.495599
## 2023.163 3.687755 5.495599
## 2023.163 3.687755 5.495599
## 2023.163 3.687755 5.495599
## 2023.163 3.687755 5.495599
## 2023.163 3.687755 5.495599
## 2023.163 3.687755 5.495599
## 2023.164 3.687755 5.495599
## 2023.164 3.687755 5.495599
## 2023.164 3.687755 5.495599
## 2023.164 3.687755 5.495599
## 2023.164 3.687755 5.495599
## 2023.164 3.687755 5.495599
## 2023.164 3.687755 5.495599
## 2023.164 3.687755 5.495599
## 2023.164 3.687755 5.495599
## 2023.164 3.687755 5.495599
## 2023.165 3.687755 5.495599
## 2023.165 3.687755 5.495599
## 2023.165 3.687755 5.495599
## 2023.165 3.687755 5.495599
## 2023.165 3.687755 5.495599
## 2023.165 3.687755 5.495599
## 2023.165 3.687755 5.495599
## 2023.165 3.687755 5.495599
## 2023.165 3.687755 5.495599
## 2023.166 3.687755 5.495599
## 2023.166 3.687755 5.495599
## 2023.166 3.687755 5.495599
## 2023.166 3.687755 5.495599
## 2023.166 3.687755 5.495599
## 2023.166 3.687755 5.495599
## 2023.166 3.687755 5.495599
## 2023.166 3.687755 5.495599
## 2023.166 3.687755 5.495599
## 2023.166 3.687755 5.495599
## 2023.167 3.687755 5.495599
## 2023.167 3.687755 5.495599
## 2023.167 3.687755 5.495599
## 2023.167 3.687755 5.495599
## 2023.167 3.687755 5.495599
## 2023.167 3.687755 5.495599
## 2023.167 3.687755 5.495599
## 2023.167 3.687755 5.495599
## 2023.167 3.687755 5.495599
## 2023.168 3.687755 5.495599
## 2023.168 3.687755 5.495599
## 2023.168 3.687755 5.495599
## 2023.168 3.687755 5.495599
## 2023.168 3.687755 5.495599
## 2023.168 3.687755 5.495599
## 2023.168 3.687755 5.495599
## 2023.168 3.687755 5.495599
## 2023.168 3.687755 5.495599
## 2023.168 3.687755 5.495599
## 2023.169 3.687755 5.495599
## 2023.169 3.687755 5.495599
## 2023.169 3.687755 5.495599
## 2023.169 3.687755 5.495599
## 2023.169 3.687755 5.495599
## 2023.169 3.687755 5.495599
## 2023.169 3.687755 5.495599
## 2023.169 3.687755 5.495599
## 2023.169 3.687755 5.495599
## 2023.170 3.687755 5.495599
## 2023.170 3.687755 5.495599
## 2023.170 3.687755 5.495599
## 2023.170 3.687755 5.495599
## 2023.170 3.687755 5.495599
## 2023.170 3.687755 5.495599
## 2023.170 3.687755 5.495599
## 2023.170 3.687755 5.495599
## 2023.170 3.687755 5.495599
## 2023.170 3.687755 5.495599
## 2023.171 3.687755 5.495599
## 2023.171 3.687755 5.495599
## 2023.171 3.687755 5.495599
## 2023.171 3.687755 5.495599
## 2023.171 3.687755 5.495599
## 2023.171 3.687755 5.495599
## 2023.171 3.687755 5.495599
## 2023.171 3.687755 5.495599
## 2023.171 3.687755 5.495599
## 2023.172 3.687755 5.495599
## 2023.172 3.687755 5.495599
## 2023.172 3.687755 5.495599
## 2023.172 3.687755 5.495599
## 2023.172 3.687755 5.495599
## 2023.172 3.687755 5.495599
## 2023.172 3.687755 5.495599
## 2023.172 3.687755 5.495599
## 2023.172 3.687755 5.495599
## 2023.172 3.687755 5.495599
## 2023.173 3.687755 5.495599
## 2023.173 3.687755 5.495599
## 2023.173 3.687755 5.495599
## 2023.173 3.687755 5.495599
## 2023.173 3.687755 5.495599
## 2023.173 3.687755 5.495599
## 2023.173 3.687755 5.495599
## 2023.173 3.687755 5.495599
## 2023.173 3.687755 5.495599
## 2023.174 3.687755 5.495599
## 2023.174 3.687755 5.495599
## 2023.174 3.687755 5.495599
## 2023.174 3.687755 5.495599
## 2023.174 3.687755 5.495599
## 2023.174 3.687755 5.495599
## 2023.174 3.687755 5.495599
## 2023.174 3.687755 5.495599
## 2023.174 3.687755 5.495599
## 2023.174 3.687755 5.495599
## 2023.175 3.687755 5.495599
## 2023.175 3.687755 5.495599
## 2023.175 3.687755 5.495599
## 2023.175 3.687755 5.495599
## 2023.175 3.687755 5.495599
## 2023.175 3.687755 5.495599
## 2023.175 3.687755 5.495599
## 2023.175 3.687755 5.495599
## 2023.175 3.687755 5.495599
## 2023.176 3.687755 5.495599
## 2023.176 3.687755 5.495599
## 2023.176 3.687755 5.495599
## 2023.176 3.687755 5.495599
## 2023.176 3.687755 5.495599
## 2023.176 3.687755 5.495599
## 2023.176 3.687755 5.495599
## 2023.176 3.687755 5.495599
## 2023.176 3.687755 5.495599
## 2023.176 3.687755 5.495599
## 2023.177 3.687755 5.495599
## 2023.177 3.687755 5.495599
## 2023.177 3.687755 5.495599
## 2023.177 3.687755 5.495599
## 2023.177 3.687755 5.495599
## 2023.177 3.687755 5.495599
## 2023.177 3.687755 5.495599
## 2023.177 3.687755 5.495599
## 2023.177 3.687755 5.495599
## 2023.178 3.687755 5.495599
## 2023.178 3.687755 5.495599
## 2023.178 3.687755 5.495599
## 2023.178 3.687755 5.495599
## 2023.178 3.687755 5.495599
## 2023.178 3.687755 5.495599
## 2023.178 3.687755 5.495599
## 2023.178 3.687755 5.495599
## 2023.178 3.687755 5.495599
## 2023.178 3.687755 5.495599
## 2023.179 3.687755 5.495599
## 2023.179 3.687755 5.495599
## 2023.179 3.687755 5.495599
## 2023.179 3.687755 5.495599
## 2023.179 3.687755 5.495599
## 2023.179 3.687755 5.495599
## 2023.179 3.687755 5.495599head(predict_precipprob)## $method
## [1] "ARIMA(5,0,0) with non-zero mean"
##
## $model
## Series: data_precipprob
## ARIMA(5,0,0) with non-zero mean
##
## Coefficients:
## ar1 ar2 ar3 ar4 ar5 mean
## -0.0433 -0.0457 -0.0482 -0.0483 -0.0486 3.9668
## s.e. 0.0102 0.0102 0.0102 0.0102 0.0102 0.1615
##
## sigma^2 = 377.6: log likelihood = -41680.59
## AIC=83375.18 AICc=83375.19 BIC=83425.3
##
## $level
## [1] 80 95
##
## $mean
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## [1] 4.895384 4.683530 4.469042 4.251985 4.044834 3.846499 3.895667 3.933495
## [9] 3.959728 3.974140 3.977752 3.971457 3.967764 3.966067 3.965738 3.966136
## [17] 3.966700 3.966935 3.966978 3.966935 3.966877 3.966841 3.966833 3.966838
## [25] 3.966845 3.966849 3.966851 3.966850 3.966849 3.966849 3.966849 3.966849
## [33] 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849
## [41] 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849
## [49] 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849
## [57] 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849
## [65] 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849
## [73] 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849
## [81] 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849
## [89] 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849
## [97] 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849
## [105] 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849
## [113] 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849
## [121] 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849
## [129] 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849
## [137] 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849
## [145] 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849
## [153] 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849
## [161] 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849 3.966849
##
## $lower
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## 80% 95%
## 2023.162 -20.00868 -33.19210
## 2023.162 -20.24385 -33.43960
## 2023.162 -20.48221 -33.69060
## 2023.162 -20.72369 -33.94501
## 2023.162 -20.95306 -34.18614
## 2023.162 -21.17175 -34.41561
## 2023.162 -21.12383 -34.36835
## 2023.162 -21.08674 -34.33166
## 2023.162 -21.06087 -34.30597
## 2023.163 -21.04656 -34.29172
## 2023.163 -21.04296 -34.28812
## 2023.163 -21.04927 -34.29444
## 2023.163 -21.05297 -34.29815
## 2023.163 -21.05467 -34.29985
## 2023.163 -21.05500 -34.30018
## 2023.163 -21.05460 -34.29978
## 2023.163 -21.05404 -34.29921
## 2023.163 -21.05380 -34.29898
## 2023.164 -21.05376 -34.29894
## 2023.164 -21.05380 -34.29898
## 2023.164 -21.05386 -34.29904
## 2023.164 -21.05390 -34.29907
## 2023.164 -21.05390 -34.29908
## 2023.164 -21.05390 -34.29908
## 2023.164 -21.05389 -34.29907
## 2023.164 -21.05389 -34.29907
## 2023.164 -21.05389 -34.29906
## 2023.164 -21.05389 -34.29906
## 2023.165 -21.05389 -34.29907
## 2023.165 -21.05389 -34.29907
## 2023.165 -21.05389 -34.29907
## 2023.165 -21.05389 -34.29907
## 2023.165 -21.05389 -34.29907
## 2023.165 -21.05389 -34.29907
## 2023.165 -21.05389 -34.29907
## 2023.165 -21.05389 -34.29907
## 2023.165 -21.05389 -34.29907
## 2023.166 -21.05389 -34.29907
## 2023.166 -21.05389 -34.29907
## 2023.166 -21.05389 -34.29907
## 2023.166 -21.05389 -34.29907
## 2023.166 -21.05389 -34.29907
## 2023.166 -21.05389 -34.29907
## 2023.166 -21.05389 -34.29907
## 2023.166 -21.05389 -34.29907
## 2023.166 -21.05389 -34.29907
## 2023.166 -21.05389 -34.29907
## 2023.167 -21.05389 -34.29907
## 2023.167 -21.05389 -34.29907
## 2023.167 -21.05389 -34.29907
## 2023.167 -21.05389 -34.29907
## 2023.167 -21.05389 -34.29907
## 2023.167 -21.05389 -34.29907
## 2023.167 -21.05389 -34.29907
## 2023.167 -21.05389 -34.29907
## 2023.167 -21.05389 -34.29907
## 2023.168 -21.05389 -34.29907
## 2023.168 -21.05389 -34.29907
## 2023.168 -21.05389 -34.29907
## 2023.168 -21.05389 -34.29907
## 2023.168 -21.05389 -34.29907
## 2023.168 -21.05389 -34.29907
## 2023.168 -21.05389 -34.29907
## 2023.168 -21.05389 -34.29907
## 2023.168 -21.05389 -34.29907
## 2023.168 -21.05389 -34.29907
## 2023.169 -21.05389 -34.29907
## 2023.169 -21.05389 -34.29907
## 2023.169 -21.05389 -34.29907
## 2023.169 -21.05389 -34.29907
## 2023.169 -21.05389 -34.29907
## 2023.169 -21.05389 -34.29907
## 2023.169 -21.05389 -34.29907
## 2023.169 -21.05389 -34.29907
## 2023.169 -21.05389 -34.29907
## 2023.170 -21.05389 -34.29907
## 2023.170 -21.05389 -34.29907
## 2023.170 -21.05389 -34.29907
## 2023.170 -21.05389 -34.29907
## 2023.170 -21.05389 -34.29907
## 2023.170 -21.05389 -34.29907
## 2023.170 -21.05389 -34.29907
## 2023.170 -21.05389 -34.29907
## 2023.170 -21.05389 -34.29907
## 2023.170 -21.05389 -34.29907
## 2023.171 -21.05389 -34.29907
## 2023.171 -21.05389 -34.29907
## 2023.171 -21.05389 -34.29907
## 2023.171 -21.05389 -34.29907
## 2023.171 -21.05389 -34.29907
## 2023.171 -21.05389 -34.29907
## 2023.171 -21.05389 -34.29907
## 2023.171 -21.05389 -34.29907
## 2023.171 -21.05389 -34.29907
## 2023.172 -21.05389 -34.29907
## 2023.172 -21.05389 -34.29907
## 2023.172 -21.05389 -34.29907
## 2023.172 -21.05389 -34.29907
## 2023.172 -21.05389 -34.29907
## 2023.172 -21.05389 -34.29907
## 2023.172 -21.05389 -34.29907
## 2023.172 -21.05389 -34.29907
## 2023.172 -21.05389 -34.29907
## 2023.172 -21.05389 -34.29907
## 2023.173 -21.05389 -34.29907
## 2023.173 -21.05389 -34.29907
## 2023.173 -21.05389 -34.29907
## 2023.173 -21.05389 -34.29907
## 2023.173 -21.05389 -34.29907
## 2023.173 -21.05389 -34.29907
## 2023.173 -21.05389 -34.29907
## 2023.173 -21.05389 -34.29907
## 2023.173 -21.05389 -34.29907
## 2023.174 -21.05389 -34.29907
## 2023.174 -21.05389 -34.29907
## 2023.174 -21.05389 -34.29907
## 2023.174 -21.05389 -34.29907
## 2023.174 -21.05389 -34.29907
## 2023.174 -21.05389 -34.29907
## 2023.174 -21.05389 -34.29907
## 2023.174 -21.05389 -34.29907
## 2023.174 -21.05389 -34.29907
## 2023.174 -21.05389 -34.29907
## 2023.175 -21.05389 -34.29907
## 2023.175 -21.05389 -34.29907
## 2023.175 -21.05389 -34.29907
## 2023.175 -21.05389 -34.29907
## 2023.175 -21.05389 -34.29907
## 2023.175 -21.05389 -34.29907
## 2023.175 -21.05389 -34.29907
## 2023.175 -21.05389 -34.29907
## 2023.175 -21.05389 -34.29907
## 2023.176 -21.05389 -34.29907
## 2023.176 -21.05389 -34.29907
## 2023.176 -21.05389 -34.29907
## 2023.176 -21.05389 -34.29907
## 2023.176 -21.05389 -34.29907
## 2023.176 -21.05389 -34.29907
## 2023.176 -21.05389 -34.29907
## 2023.176 -21.05389 -34.29907
## 2023.176 -21.05389 -34.29907
## 2023.176 -21.05389 -34.29907
## 2023.177 -21.05389 -34.29907
## 2023.177 -21.05389 -34.29907
## 2023.177 -21.05389 -34.29907
## 2023.177 -21.05389 -34.29907
## 2023.177 -21.05389 -34.29907
## 2023.177 -21.05389 -34.29907
## 2023.177 -21.05389 -34.29907
## 2023.177 -21.05389 -34.29907
## 2023.177 -21.05389 -34.29907
## 2023.178 -21.05389 -34.29907
## 2023.178 -21.05389 -34.29907
## 2023.178 -21.05389 -34.29907
## 2023.178 -21.05389 -34.29907
## 2023.178 -21.05389 -34.29907
## 2023.178 -21.05389 -34.29907
## 2023.178 -21.05389 -34.29907
## 2023.178 -21.05389 -34.29907
## 2023.178 -21.05389 -34.29907
## 2023.178 -21.05389 -34.29907
## 2023.179 -21.05389 -34.29907
## 2023.179 -21.05389 -34.29907
## 2023.179 -21.05389 -34.29907
## 2023.179 -21.05389 -34.29907
## 2023.179 -21.05389 -34.29907
## 2023.179 -21.05389 -34.29907
## 2023.179 -21.05389 -34.29907
##
## $upper
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## 80% 95%
## 2023.162 29.79945 42.98287
## 2023.162 29.61091 42.80666
## 2023.162 29.42029 42.62868
## 2023.162 29.22765 42.44898
## 2023.162 29.04273 42.27581
## 2023.162 28.86475 42.10861
## 2023.162 28.91516 42.15969
## 2023.162 28.95373 42.19865
## 2023.162 28.98032 42.22542
## 2023.163 28.99484 42.24000
## 2023.163 28.99846 42.24362
## 2023.163 28.99218 42.23736
## 2023.163 28.98850 42.23368
## 2023.163 28.98680 42.23198
## 2023.163 28.98647 42.23165
## 2023.163 28.98687 42.23205
## 2023.163 28.98744 42.23261
## 2023.163 28.98767 42.23285
## 2023.164 28.98771 42.23289
## 2023.164 28.98767 42.23285
## 2023.164 28.98761 42.23279
## 2023.164 28.98758 42.23276
## 2023.164 28.98757 42.23275
## 2023.164 28.98757 42.23275
## 2023.164 28.98758 42.23276
## 2023.164 28.98759 42.23276
## 2023.164 28.98759 42.23277
## 2023.164 28.98759 42.23277
## 2023.165 28.98759 42.23276
## 2023.165 28.98759 42.23276
## 2023.165 28.98759 42.23276
## 2023.165 28.98759 42.23276
## 2023.165 28.98759 42.23276
## 2023.165 28.98759 42.23276
## 2023.165 28.98759 42.23276
## 2023.165 28.98759 42.23276
## 2023.165 28.98759 42.23276
## 2023.166 28.98759 42.23276
## 2023.166 28.98759 42.23276
## 2023.166 28.98759 42.23276
## 2023.166 28.98759 42.23276
## 2023.166 28.98759 42.23276
## 2023.166 28.98759 42.23276
## 2023.166 28.98759 42.23276
## 2023.166 28.98759 42.23276
## 2023.166 28.98759 42.23276
## 2023.166 28.98759 42.23276
## 2023.167 28.98759 42.23276
## 2023.167 28.98759 42.23276
## 2023.167 28.98759 42.23276
## 2023.167 28.98759 42.23276
## 2023.167 28.98759 42.23276
## 2023.167 28.98759 42.23276
## 2023.167 28.98759 42.23276
## 2023.167 28.98759 42.23276
## 2023.167 28.98759 42.23276
## 2023.168 28.98759 42.23276
## 2023.168 28.98759 42.23276
## 2023.168 28.98759 42.23276
## 2023.168 28.98759 42.23276
## 2023.168 28.98759 42.23276
## 2023.168 28.98759 42.23276
## 2023.168 28.98759 42.23276
## 2023.168 28.98759 42.23276
## 2023.168 28.98759 42.23276
## 2023.168 28.98759 42.23276
## 2023.169 28.98759 42.23276
## 2023.169 28.98759 42.23276
## 2023.169 28.98759 42.23276
## 2023.169 28.98759 42.23276
## 2023.169 28.98759 42.23276
## 2023.169 28.98759 42.23276
## 2023.169 28.98759 42.23276
## 2023.169 28.98759 42.23276
## 2023.169 28.98759 42.23276
## 2023.170 28.98759 42.23276
## 2023.170 28.98759 42.23276
## 2023.170 28.98759 42.23276
## 2023.170 28.98759 42.23276
## 2023.170 28.98759 42.23276
## 2023.170 28.98759 42.23276
## 2023.170 28.98759 42.23276
## 2023.170 28.98759 42.23276
## 2023.170 28.98759 42.23276
## 2023.170 28.98759 42.23276
## 2023.171 28.98759 42.23276
## 2023.171 28.98759 42.23276
## 2023.171 28.98759 42.23276
## 2023.171 28.98759 42.23276
## 2023.171 28.98759 42.23276
## 2023.171 28.98759 42.23276
## 2023.171 28.98759 42.23276
## 2023.171 28.98759 42.23276
## 2023.171 28.98759 42.23276
## 2023.172 28.98759 42.23276
## 2023.172 28.98759 42.23276
## 2023.172 28.98759 42.23276
## 2023.172 28.98759 42.23276
## 2023.172 28.98759 42.23276
## 2023.172 28.98759 42.23276
## 2023.172 28.98759 42.23276
## 2023.172 28.98759 42.23276
## 2023.172 28.98759 42.23276
## 2023.172 28.98759 42.23276
## 2023.173 28.98759 42.23276
## 2023.173 28.98759 42.23276
## 2023.173 28.98759 42.23276
## 2023.173 28.98759 42.23276
## 2023.173 28.98759 42.23276
## 2023.173 28.98759 42.23276
## 2023.173 28.98759 42.23276
## 2023.173 28.98759 42.23276
## 2023.173 28.98759 42.23276
## 2023.174 28.98759 42.23276
## 2023.174 28.98759 42.23276
## 2023.174 28.98759 42.23276
## 2023.174 28.98759 42.23276
## 2023.174 28.98759 42.23276
## 2023.174 28.98759 42.23276
## 2023.174 28.98759 42.23276
## 2023.174 28.98759 42.23276
## 2023.174 28.98759 42.23276
## 2023.174 28.98759 42.23276
## 2023.175 28.98759 42.23276
## 2023.175 28.98759 42.23276
## 2023.175 28.98759 42.23276
## 2023.175 28.98759 42.23276
## 2023.175 28.98759 42.23276
## 2023.175 28.98759 42.23276
## 2023.175 28.98759 42.23276
## 2023.175 28.98759 42.23276
## 2023.175 28.98759 42.23276
## 2023.176 28.98759 42.23276
## 2023.176 28.98759 42.23276
## 2023.176 28.98759 42.23276
## 2023.176 28.98759 42.23276
## 2023.176 28.98759 42.23276
## 2023.176 28.98759 42.23276
## 2023.176 28.98759 42.23276
## 2023.176 28.98759 42.23276
## 2023.176 28.98759 42.23276
## 2023.176 28.98759 42.23276
## 2023.177 28.98759 42.23276
## 2023.177 28.98759 42.23276
## 2023.177 28.98759 42.23276
## 2023.177 28.98759 42.23276
## 2023.177 28.98759 42.23276
## 2023.177 28.98759 42.23276
## 2023.177 28.98759 42.23276
## 2023.177 28.98759 42.23276
## 2023.177 28.98759 42.23276
## 2023.178 28.98759 42.23276
## 2023.178 28.98759 42.23276
## 2023.178 28.98759 42.23276
## 2023.178 28.98759 42.23276
## 2023.178 28.98759 42.23276
## 2023.178 28.98759 42.23276
## 2023.178 28.98759 42.23276
## 2023.178 28.98759 42.23276
## 2023.178 28.98759 42.23276
## 2023.178 28.98759 42.23276
## 2023.179 28.98759 42.23276
## 2023.179 28.98759 42.23276
## 2023.179 28.98759 42.23276
## 2023.179 28.98759 42.23276
## 2023.179 28.98759 42.23276
## 2023.179 28.98759 42.23276
## 2023.179 28.98759 42.23276head(predict_windgust)## $method
## [1] "ARIMA(2,1,1)"
##
## $model
## Series: data_windgust
## ARIMA(2,1,1)
##
## Coefficients:
## ar1 ar2 ma1
## 0.5145 0.1542 -0.9878
## s.e. 0.0106 0.0106 0.0029
##
## sigma^2 = 11.3: log likelihood = -25006.14
## AIC=50020.28 AICc=50020.28 BIC=50048.91
##
## $level
## [1] 80 95
##
## $mean
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## [1] 3.415041 3.932108 4.339261 4.628488 4.840088 4.993563 5.105161 5.186248
## [9] 5.245178 5.288004 5.319126 5.341743 5.358180 5.370125 5.378805 5.385113
## [17] 5.389698 5.393029 5.395451 5.397210 5.398489 5.399418 5.400093 5.400584
## [25] 5.400941 5.401200 5.401388 5.401525 5.401625 5.401697 5.401749 5.401788
## [33] 5.401815 5.401835 5.401850 5.401861 5.401869 5.401874 5.401878 5.401881
## [41] 5.401883 5.401885 5.401886 5.401887 5.401887 5.401888 5.401888 5.401888
## [49] 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889
## [57] 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889
## [65] 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889
## [73] 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889
## [81] 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889
## [89] 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889
## [97] 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889
## [105] 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889
## [113] 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889
## [121] 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889
## [129] 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889
## [137] 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889
## [145] 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889
## [153] 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889
## [161] 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889 5.401889
##
## $lower
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## 80% 95%
## 2023.162 -0.8933978 -3.174148
## 2023.162 -0.9374888 -3.515298
## 2023.162 -0.8823585 -3.646517
## 2023.162 -0.7704745 -3.628512
## 2023.162 -0.6599254 -3.571457
## 2023.162 -0.5649456 -3.507443
## 2023.162 -0.4887491 -3.449986
## 2023.162 -0.4299633 -3.403006
## 2023.162 -0.3857487 -3.366581
## 2023.163 -0.3531247 -3.339358
## 2023.163 -0.3294460 -3.319619
## 2023.163 -0.3125422 -3.305740
## 2023.163 -0.3007061 -3.296339
## 2023.163 -0.2926285 -3.290309
## 2023.163 -0.2873223 -3.286789
## 2023.163 -0.2840510 -3.285125
## 2023.163 -0.2822695 -3.284828
## 2023.163 -0.2815763 -3.285531
## 2023.164 -0.2816770 -3.286967
## 2023.164 -0.2823560 -3.288937
## 2023.164 -0.2834559 -3.291296
## 2023.164 -0.2848619 -3.293938
## 2023.164 -0.2864905 -3.296786
## 2023.164 -0.2882806 -3.299784
## 2023.164 -0.2901880 -3.302889
## 2023.164 -0.2921804 -3.306074
## 2023.164 -0.2942343 -3.309315
## 2023.164 -0.2963328 -3.312597
## 2023.165 -0.2984635 -3.315908
## 2023.165 -0.3006172 -3.319240
## 2023.165 -0.3027875 -3.322587
## 2023.165 -0.3049696 -3.325944
## 2023.165 -0.3071601 -3.329309
## 2023.165 -0.3093564 -3.332679
## 2023.165 -0.3115567 -3.336052
## 2023.165 -0.3137597 -3.339426
## 2023.165 -0.3159644 -3.342802
## 2023.166 -0.3181701 -3.346179
## 2023.166 -0.3203763 -3.349555
## 2023.166 -0.3225826 -3.352931
## 2023.166 -0.3247888 -3.356306
## 2023.166 -0.3269947 -3.359680
## 2023.166 -0.3292001 -3.363054
## 2023.166 -0.3314049 -3.366426
## 2023.166 -0.3336091 -3.369798
## 2023.166 -0.3358126 -3.373168
## 2023.166 -0.3380153 -3.376537
## 2023.167 -0.3402173 -3.379904
## 2023.167 -0.3424184 -3.383271
## 2023.167 -0.3446188 -3.386636
## 2023.167 -0.3468184 -3.390000
## 2023.167 -0.3490171 -3.393363
## 2023.167 -0.3512150 -3.396724
## 2023.167 -0.3534121 -3.400085
## 2023.167 -0.3556084 -3.403443
## 2023.167 -0.3578038 -3.406801
## 2023.168 -0.3599984 -3.410157
## 2023.168 -0.3621922 -3.413512
## 2023.168 -0.3643851 -3.416866
## 2023.168 -0.3665772 -3.420219
## 2023.168 -0.3687685 -3.423570
## 2023.168 -0.3709589 -3.426920
## 2023.168 -0.3731485 -3.430269
## 2023.168 -0.3753373 -3.433616
## 2023.168 -0.3775252 -3.436962
## 2023.168 -0.3797124 -3.440307
## 2023.169 -0.3818987 -3.443651
## 2023.169 -0.3840841 -3.446993
## 2023.169 -0.3862688 -3.450334
## 2023.169 -0.3884526 -3.453674
## 2023.169 -0.3906356 -3.457013
## 2023.169 -0.3928178 -3.460350
## 2023.169 -0.3949991 -3.463686
## 2023.169 -0.3971796 -3.467021
## 2023.169 -0.3993593 -3.470355
## 2023.170 -0.4015382 -3.473687
## 2023.170 -0.4037163 -3.477018
## 2023.170 -0.4058936 -3.480348
## 2023.170 -0.4080700 -3.483677
## 2023.170 -0.4102456 -3.487004
## 2023.170 -0.4124205 -3.490330
## 2023.170 -0.4145945 -3.493655
## 2023.170 -0.4167676 -3.496978
## 2023.170 -0.4189400 -3.500301
## 2023.170 -0.4211116 -3.503622
## 2023.171 -0.4232823 -3.506942
## 2023.171 -0.4254523 -3.510260
## 2023.171 -0.4276214 -3.513578
## 2023.171 -0.4297898 -3.516894
## 2023.171 -0.4319573 -3.520209
## 2023.171 -0.4341240 -3.523523
## 2023.171 -0.4362899 -3.526835
## 2023.171 -0.4384550 -3.530146
## 2023.171 -0.4406194 -3.533457
## 2023.172 -0.4427829 -3.536765
## 2023.172 -0.4449456 -3.540073
## 2023.172 -0.4471075 -3.543379
## 2023.172 -0.4492686 -3.546684
## 2023.172 -0.4514289 -3.549988
## 2023.172 -0.4535884 -3.553291
## 2023.172 -0.4557472 -3.556592
## 2023.172 -0.4579051 -3.559893
## 2023.172 -0.4600622 -3.563192
## 2023.172 -0.4622186 -3.566490
## 2023.173 -0.4643741 -3.569786
## 2023.173 -0.4665289 -3.573082
## 2023.173 -0.4686828 -3.576376
## 2023.173 -0.4708360 -3.579669
## 2023.173 -0.4729884 -3.582961
## 2023.173 -0.4751400 -3.586251
## 2023.173 -0.4772908 -3.589541
## 2023.173 -0.4794408 -3.592829
## 2023.173 -0.4815900 -3.596116
## 2023.174 -0.4837385 -3.599402
## 2023.174 -0.4858862 -3.602686
## 2023.174 -0.4880330 -3.605969
## 2023.174 -0.4901791 -3.609252
## 2023.174 -0.4923245 -3.612533
## 2023.174 -0.4944690 -3.615812
## 2023.174 -0.4966128 -3.619091
## 2023.174 -0.4987557 -3.622368
## 2023.174 -0.5008979 -3.625645
## 2023.174 -0.5030394 -3.628920
## 2023.175 -0.5051800 -3.632194
## 2023.175 -0.5073199 -3.635466
## 2023.175 -0.5094590 -3.638738
## 2023.175 -0.5115973 -3.642008
## 2023.175 -0.5137349 -3.645277
## 2023.175 -0.5158716 -3.648545
## 2023.175 -0.5180077 -3.651812
## 2023.175 -0.5201429 -3.655077
## 2023.175 -0.5222774 -3.658342
## 2023.176 -0.5244111 -3.661605
## 2023.176 -0.5265440 -3.664867
## 2023.176 -0.5286762 -3.668128
## 2023.176 -0.5308076 -3.671387
## 2023.176 -0.5329382 -3.674646
## 2023.176 -0.5350681 -3.677903
## 2023.176 -0.5371972 -3.681160
## 2023.176 -0.5393255 -3.684415
## 2023.176 -0.5414531 -3.687668
## 2023.176 -0.5435799 -3.690921
## 2023.177 -0.5457060 -3.694173
## 2023.177 -0.5478313 -3.697423
## 2023.177 -0.5499558 -3.700672
## 2023.177 -0.5520796 -3.703920
## 2023.177 -0.5542026 -3.707167
## 2023.177 -0.5563249 -3.710413
## 2023.177 -0.5584464 -3.713657
## 2023.177 -0.5605671 -3.716901
## 2023.177 -0.5626872 -3.720143
## 2023.178 -0.5648064 -3.723384
## 2023.178 -0.5669249 -3.726624
## 2023.178 -0.5690426 -3.729863
## 2023.178 -0.5711596 -3.733101
## 2023.178 -0.5732759 -3.736337
## 2023.178 -0.5753914 -3.739573
## 2023.178 -0.5775061 -3.742807
## 2023.178 -0.5796201 -3.746040
## 2023.178 -0.5817334 -3.749272
## 2023.178 -0.5838459 -3.752503
## 2023.179 -0.5859576 -3.755732
## 2023.179 -0.5880687 -3.758961
## 2023.179 -0.5901789 -3.762188
## 2023.179 -0.5922885 -3.765414
## 2023.179 -0.5943973 -3.768640
## 2023.179 -0.5965053 -3.771863
## 2023.179 -0.5986126 -3.775086
##
## $upper
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## 80% 95%
## 2023.162 7.723480 10.00423
## 2023.162 8.801706 11.37951
## 2023.162 9.560881 12.32504
## 2023.162 10.027450 12.88549
## 2023.162 10.340102 13.25163
## 2023.162 10.552072 13.49457
## 2023.162 10.699070 13.66031
## 2023.162 10.802458 13.77550
## 2023.162 10.876105 13.85694
## 2023.163 10.929132 13.91536
## 2023.163 10.967698 13.95787
## 2023.163 10.996029 13.98923
## 2023.163 11.017066 14.01270
## 2023.163 11.032878 14.03056
## 2023.163 11.044932 14.04440
## 2023.163 11.054278 14.05535
## 2023.163 11.061665 14.06422
## 2023.163 11.067635 14.07159
## 2023.164 11.072578 14.07787
## 2023.164 11.076776 14.08336
## 2023.164 11.080433 14.08827
## 2023.164 11.083698 14.09277
## 2023.164 11.086677 14.09697
## 2023.164 11.089449 14.10095
## 2023.164 11.092069 14.10477
## 2023.164 11.094580 14.10847
## 2023.164 11.097011 14.11209
## 2023.164 11.099383 14.11565
## 2023.165 11.101713 14.11916
## 2023.165 11.104011 14.12263
## 2023.165 11.106286 14.12609
## 2023.165 11.108545 14.12952
## 2023.165 11.110791 14.13294
## 2023.165 11.113027 14.13635
## 2023.165 11.115257 14.13975
## 2023.165 11.117481 14.14315
## 2023.165 11.119701 14.14654
## 2023.166 11.121918 14.14993
## 2023.166 11.124133 14.15331
## 2023.166 11.126345 14.15669
## 2023.166 11.128556 14.16007
## 2023.166 11.130765 14.16345
## 2023.166 11.132972 14.16683
## 2023.166 11.135179 14.17020
## 2023.166 11.137384 14.17357
## 2023.166 11.139588 14.17694
## 2023.166 11.141792 14.18031
## 2023.167 11.143994 14.18368
## 2023.167 11.146196 14.18705
## 2023.167 11.148396 14.19041
## 2023.167 11.150596 14.19378
## 2023.167 11.152795 14.19714
## 2023.167 11.154993 14.20050
## 2023.167 11.157190 14.20386
## 2023.167 11.159386 14.20722
## 2023.167 11.161582 14.21058
## 2023.168 11.163777 14.21394
## 2023.168 11.165970 14.21729
## 2023.168 11.168163 14.22064
## 2023.168 11.170355 14.22400
## 2023.168 11.172547 14.22735
## 2023.168 11.174737 14.23070
## 2023.168 11.176927 14.23405
## 2023.168 11.179115 14.23739
## 2023.168 11.181303 14.24074
## 2023.168 11.183491 14.24409
## 2023.169 11.185677 14.24743
## 2023.169 11.187862 14.25077
## 2023.169 11.190047 14.25411
## 2023.169 11.192231 14.25745
## 2023.169 11.194414 14.26079
## 2023.169 11.196596 14.26413
## 2023.169 11.198777 14.26746
## 2023.169 11.200958 14.27080
## 2023.169 11.203138 14.27413
## 2023.170 11.205316 14.27747
## 2023.170 11.207495 14.28080
## 2023.170 11.209672 14.28413
## 2023.170 11.211848 14.28745
## 2023.170 11.214024 14.29078
## 2023.170 11.216199 14.29411
## 2023.170 11.218373 14.29743
## 2023.170 11.220546 14.30076
## 2023.170 11.222718 14.30408
## 2023.170 11.224890 14.30740
## 2023.171 11.227061 14.31072
## 2023.171 11.229230 14.31404
## 2023.171 11.231400 14.31736
## 2023.171 11.233568 14.32067
## 2023.171 11.235735 14.32399
## 2023.171 11.237902 14.32730
## 2023.171 11.240068 14.33061
## 2023.171 11.242233 14.33392
## 2023.171 11.244398 14.33723
## 2023.172 11.246561 14.34054
## 2023.172 11.248724 14.34385
## 2023.172 11.250886 14.34716
## 2023.172 11.253047 14.35046
## 2023.172 11.255207 14.35377
## 2023.172 11.257367 14.35707
## 2023.172 11.259525 14.36037
## 2023.172 11.261683 14.36367
## 2023.172 11.263840 14.36697
## 2023.172 11.265997 14.37027
## 2023.173 11.268152 14.37356
## 2023.173 11.270307 14.37686
## 2023.173 11.272461 14.38015
## 2023.173 11.274614 14.38345
## 2023.173 11.276767 14.38674
## 2023.173 11.278918 14.39003
## 2023.173 11.281069 14.39332
## 2023.173 11.283219 14.39661
## 2023.173 11.285368 14.39989
## 2023.174 11.287517 14.40318
## 2023.174 11.289664 14.40646
## 2023.174 11.291811 14.40975
## 2023.174 11.293957 14.41303
## 2023.174 11.296103 14.41631
## 2023.174 11.298247 14.41959
## 2023.174 11.300391 14.42287
## 2023.174 11.302534 14.42615
## 2023.174 11.304676 14.42942
## 2023.174 11.306818 14.43270
## 2023.175 11.308958 14.43597
## 2023.175 11.311098 14.43924
## 2023.175 11.313237 14.44252
## 2023.175 11.315376 14.44579
## 2023.175 11.317513 14.44906
## 2023.175 11.319650 14.45232
## 2023.175 11.321786 14.45559
## 2023.175 11.323921 14.45886
## 2023.175 11.326056 14.46212
## 2023.176 11.328189 14.46538
## 2023.176 11.330322 14.46865
## 2023.176 11.332454 14.47191
## 2023.176 11.334586 14.47517
## 2023.176 11.336716 14.47842
## 2023.176 11.338846 14.48168
## 2023.176 11.340975 14.48494
## 2023.176 11.343104 14.48819
## 2023.176 11.345231 14.49145
## 2023.176 11.347358 14.49470
## 2023.177 11.349484 14.49795
## 2023.177 11.351609 14.50120
## 2023.177 11.353734 14.50445
## 2023.177 11.355858 14.50770
## 2023.177 11.357981 14.51095
## 2023.177 11.360103 14.51419
## 2023.177 11.362225 14.51744
## 2023.177 11.364345 14.52068
## 2023.177 11.366465 14.52392
## 2023.178 11.368585 14.52716
## 2023.178 11.370703 14.53040
## 2023.178 11.372821 14.53364
## 2023.178 11.374938 14.53688
## 2023.178 11.377054 14.54012
## 2023.178 11.379170 14.54335
## 2023.178 11.381284 14.54658
## 2023.178 11.383398 14.54982
## 2023.178 11.385512 14.55305
## 2023.178 11.387624 14.55628
## 2023.179 11.389736 14.55951
## 2023.179 11.391847 14.56274
## 2023.179 11.393957 14.56597
## 2023.179 11.396067 14.56919
## 2023.179 11.398175 14.57242
## 2023.179 11.400283 14.57564
## 2023.179 11.402391 14.57886head(predict_windspeed)## $method
## [1] "ARIMA(0,1,4)"
##
## $model
## Series: data_windspeed
## ARIMA(0,1,4)
##
## Coefficients:
## ma1 ma2 ma3 ma4
## -0.2068 -0.0664 -0.0588 -0.0577
## s.e. 0.0107 0.0114 0.0128 0.0143
##
## sigma^2 = 11.08: log likelihood = -24910.64
## AIC=49831.27 AICc=49831.28 BIC=49867.07
##
## $level
## [1] 80 95
##
## $mean
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## [1] 0.4895663 0.8623564 1.0002948 1.0358925 1.0358925 1.0358925 1.0358925
## [8] 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925
## [15] 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925
## [22] 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925
## [29] 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925
## [36] 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925
## [43] 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925
## [50] 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925
## [57] 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925
## [64] 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925
## [71] 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925
## [78] 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925
## [85] 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925
## [92] 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925
## [99] 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925
## [106] 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925
## [113] 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925
## [120] 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925
## [127] 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925
## [134] 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925
## [141] 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925
## [148] 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925
## [155] 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925
## [162] 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925 1.0358925
##
## $lower
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## 80% 95%
## 2023.162 -3.776459 -6.034757
## 2023.162 -4.582690 -7.465123
## 2023.162 -5.265595 -8.582557
## 2023.162 -5.847457 -9.491282
## 2023.162 -6.323351 -10.219099
## 2023.162 -6.770286 -10.902628
## 2023.162 -7.192983 -11.549086
## 2023.162 -7.595003 -12.163923
## 2023.162 -7.979112 -12.751368
## 2023.163 -8.347512 -13.314786
## 2023.163 -8.701984 -13.856905
## 2023.163 -9.043998 -14.379970
## 2023.163 -9.374783 -14.885862
## 2023.163 -9.695376 -15.376167
## 2023.163 -10.006666 -15.852243
## 2023.163 -10.309417 -16.315261
## 2023.163 -10.604297 -16.766241
## 2023.163 -10.891889 -17.206076
## 2023.164 -11.172709 -17.635552
## 2023.164 -11.447212 -18.055369
## 2023.164 -11.715808 -18.466151
## 2023.164 -11.978862 -18.868457
## 2023.164 -12.236703 -19.262791
## 2023.164 -12.489630 -19.649609
## 2023.164 -12.737913 -20.029325
## 2023.164 -12.981799 -20.402317
## 2023.164 -13.221514 -20.768929
## 2023.164 -13.457265 -21.129479
## 2023.165 -13.689241 -21.484256
## 2023.165 -13.917620 -21.833531
## 2023.165 -14.142562 -22.177551
## 2023.165 -14.364220 -22.516547
## 2023.165 -14.582732 -22.850732
## 2023.165 -14.798228 -23.180306
## 2023.165 -15.010831 -23.505454
## 2023.165 -15.220654 -23.826350
## 2023.165 -15.427803 -24.143157
## 2023.166 -15.632378 -24.456027
## 2023.166 -15.834472 -24.765103
## 2023.166 -16.034174 -25.070521
## 2023.166 -16.231566 -25.372406
## 2023.166 -16.426727 -25.670880
## 2023.166 -16.619731 -25.966054
## 2023.166 -16.810648 -26.258036
## 2023.166 -16.999544 -26.546927
## 2023.166 -17.186482 -26.832824
## 2023.166 -17.371522 -27.115818
## 2023.167 -17.554720 -27.395995
## 2023.167 -17.736130 -27.673438
## 2023.167 -17.915804 -27.948225
## 2023.167 -18.093790 -28.220432
## 2023.167 -18.270135 -28.490129
## 2023.167 -18.444884 -28.757385
## 2023.167 -18.618080 -29.022265
## 2023.167 -18.789763 -29.284830
## 2023.167 -18.959971 -29.545142
## 2023.168 -19.128743 -29.803256
## 2023.168 -19.296114 -30.059228
## 2023.168 -19.462118 -30.313110
## 2023.168 -19.626789 -30.564952
## 2023.168 -19.790158 -30.814803
## 2023.168 -19.952255 -31.062709
## 2023.168 -20.113110 -31.308715
## 2023.168 -20.272750 -31.552865
## 2023.168 -20.431204 -31.795198
## 2023.168 -20.588496 -32.035756
## 2023.169 -20.744653 -32.274577
## 2023.169 -20.899698 -32.511698
## 2023.169 -21.053654 -32.747154
## 2023.169 -21.206545 -32.980981
## 2023.169 -21.358393 -33.213211
## 2023.169 -21.509217 -33.443877
## 2023.169 -21.659039 -33.673010
## 2023.169 -21.807879 -33.900641
## 2023.169 -21.955755 -34.126798
## 2023.170 -22.102686 -34.351510
## 2023.170 -22.248690 -34.574803
## 2023.170 -22.393784 -34.796706
## 2023.170 -22.537985 -35.017242
## 2023.170 -22.681309 -35.236438
## 2023.170 -22.823773 -35.454317
## 2023.170 -22.965391 -35.670902
## 2023.170 -23.106178 -35.886218
## 2023.170 -23.246149 -36.100285
## 2023.170 -23.385317 -36.313125
## 2023.171 -23.523697 -36.524759
## 2023.171 -23.661302 -36.735207
## 2023.171 -23.798144 -36.944489
## 2023.171 -23.934236 -37.152624
## 2023.171 -24.069591 -37.359631
## 2023.171 -24.204220 -37.565528
## 2023.171 -24.338134 -37.770332
## 2023.171 -24.471345 -37.974061
## 2023.171 -24.603865 -38.176732
## 2023.172 -24.735702 -38.378361
## 2023.172 -24.866869 -38.578963
## 2023.172 -24.997375 -38.778555
## 2023.172 -25.127230 -38.977151
## 2023.172 -25.256444 -39.174766
## 2023.172 -25.385026 -39.371415
## 2023.172 -25.512985 -39.567111
## 2023.172 -25.640330 -39.761869
## 2023.172 -25.767070 -39.955702
## 2023.172 -25.893214 -40.148622
## 2023.173 -26.018769 -40.340642
## 2023.173 -26.143745 -40.531776
## 2023.173 -26.268149 -40.722035
## 2023.173 -26.391988 -40.911431
## 2023.173 -26.515271 -41.099975
## 2023.173 -26.638004 -41.287680
## 2023.173 -26.760196 -41.474556
## 2023.173 -26.881852 -41.660614
## 2023.173 -27.002981 -41.845864
## 2023.174 -27.123589 -42.030318
## 2023.174 -27.243682 -42.213985
## 2023.174 -27.363268 -42.396876
## 2023.174 -27.482352 -42.578999
## 2023.174 -27.600941 -42.760365
## 2023.174 -27.719041 -42.940984
## 2023.174 -27.836658 -43.120863
## 2023.174 -27.953798 -43.300013
## 2023.174 -28.070466 -43.478441
## 2023.174 -28.186668 -43.656158
## 2023.175 -28.302411 -43.833170
## 2023.175 -28.417698 -44.009487
## 2023.175 -28.532536 -44.185116
## 2023.175 -28.646929 -44.360066
## 2023.175 -28.760884 -44.534344
## 2023.175 -28.874404 -44.707959
## 2023.175 -28.987495 -44.880916
## 2023.175 -29.100162 -45.053225
## 2023.175 -29.212409 -45.224892
## 2023.176 -29.324241 -45.395924
## 2023.176 -29.435662 -45.566329
## 2023.176 -29.546678 -45.736113
## 2023.176 -29.657292 -45.905282
## 2023.176 -29.767509 -46.073845
## 2023.176 -29.877333 -46.241806
## 2023.176 -29.986768 -46.409172
## 2023.176 -30.095818 -46.575951
## 2023.176 -30.204488 -46.742147
## 2023.176 -30.312781 -46.907767
## 2023.177 -30.420702 -47.072817
## 2023.177 -30.528253 -47.237302
## 2023.177 -30.635439 -47.401229
## 2023.177 -30.742264 -47.564603
## 2023.177 -30.848730 -47.727430
## 2023.177 -30.954843 -47.889715
## 2023.177 -31.060604 -48.051463
## 2023.177 -31.166018 -48.212680
## 2023.177 -31.271088 -48.373371
## 2023.178 -31.375818 -48.533541
## 2023.178 -31.480210 -48.693195
## 2023.178 -31.584269 -48.852339
## 2023.178 -31.687996 -49.010976
## 2023.178 -31.791395 -49.169112
## 2023.178 -31.894470 -49.326751
## 2023.178 -31.997224 -49.483899
## 2023.178 -32.099658 -49.640559
## 2023.178 -32.201777 -49.796737
## 2023.178 -32.303583 -49.952436
## 2023.179 -32.405079 -50.107661
## 2023.179 -32.506269 -50.262416
## 2023.179 -32.607153 -50.416706
## 2023.179 -32.707736 -50.570535
## 2023.179 -32.808021 -50.723906
## 2023.179 -32.908009 -50.876824
## 2023.179 -33.007703 -51.029294
##
## $upper
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## 80% 95%
## 2023.162 4.755592 7.013889
## 2023.162 6.307403 9.189836
## 2023.162 7.266185 10.583147
## 2023.162 7.919242 11.563067
## 2023.162 8.395136 12.290884
## 2023.162 8.842071 12.974413
## 2023.162 9.264768 13.620871
## 2023.162 9.666788 14.235708
## 2023.162 10.050898 14.823153
## 2023.163 10.419297 15.386571
## 2023.163 10.773769 15.928690
## 2023.163 11.115784 16.451756
## 2023.163 11.446568 16.957647
## 2023.163 11.767161 17.447952
## 2023.163 12.078451 17.924028
## 2023.163 12.381202 18.387047
## 2023.163 12.676082 18.838026
## 2023.163 12.963674 19.277861
## 2023.164 13.244494 19.707337
## 2023.164 13.518997 20.127154
## 2023.164 13.787593 20.537936
## 2023.164 14.050647 20.940242
## 2023.164 14.308488 21.334576
## 2023.164 14.561415 21.721394
## 2023.164 14.809698 22.101110
## 2023.164 15.053584 22.474102
## 2023.164 15.293299 22.840714
## 2023.164 15.529050 23.201264
## 2023.165 15.761026 23.556042
## 2023.165 15.989405 23.905316
## 2023.165 16.214347 24.249336
## 2023.165 16.436005 24.588332
## 2023.165 16.654517 24.922517
## 2023.165 16.870013 25.252091
## 2023.165 17.082616 25.577239
## 2023.165 17.292439 25.898135
## 2023.165 17.499588 26.214942
## 2023.166 17.704163 26.527812
## 2023.166 17.906257 26.836888
## 2023.166 18.105959 27.142306
## 2023.166 18.303351 27.444191
## 2023.166 18.498512 27.742665
## 2023.166 18.691516 28.037839
## 2023.166 18.882433 28.329821
## 2023.166 19.071329 28.618713
## 2023.166 19.258267 28.904610
## 2023.166 19.443307 29.187603
## 2023.167 19.626505 29.467780
## 2023.167 19.807915 29.745223
## 2023.167 19.987589 30.020011
## 2023.167 20.165575 30.292217
## 2023.167 20.341920 30.561914
## 2023.167 20.516670 30.829170
## 2023.167 20.689865 31.094050
## 2023.167 20.861548 31.356615
## 2023.167 21.031756 31.616927
## 2023.168 21.200528 31.875041
## 2023.168 21.367899 32.131013
## 2023.168 21.533903 32.384895
## 2023.168 21.698574 32.636737
## 2023.168 21.861943 32.886588
## 2023.168 22.024040 33.134494
## 2023.168 22.184895 33.380500
## 2023.168 22.344535 33.624650
## 2023.168 22.502989 33.866983
## 2023.168 22.660281 34.107541
## 2023.169 22.816438 34.346362
## 2023.169 22.971483 34.583483
## 2023.169 23.125439 34.818939
## 2023.169 23.278330 35.052766
## 2023.169 23.430178 35.284996
## 2023.169 23.581002 35.515662
## 2023.169 23.730824 35.744795
## 2023.169 23.879664 35.972426
## 2023.169 24.027540 36.198583
## 2023.170 24.174471 36.423295
## 2023.170 24.320475 36.646588
## 2023.170 24.465569 36.868491
## 2023.170 24.609770 37.089027
## 2023.170 24.753094 37.308223
## 2023.170 24.895558 37.526102
## 2023.170 25.037176 37.742687
## 2023.170 25.177963 37.958003
## 2023.170 25.317934 38.172070
## 2023.170 25.457102 38.384910
## 2023.171 25.595482 38.596544
## 2023.171 25.733087 38.806992
## 2023.171 25.869929 39.016274
## 2023.171 26.006021 39.224409
## 2023.171 26.141376 39.431416
## 2023.171 26.276005 39.637313
## 2023.171 26.409919 39.842117
## 2023.171 26.543130 40.045846
## 2023.171 26.675650 40.248517
## 2023.172 26.807488 40.450146
## 2023.172 26.938654 40.650748
## 2023.172 27.069160 40.850340
## 2023.172 27.199015 41.048936
## 2023.172 27.328229 41.246551
## 2023.172 27.456811 41.443200
## 2023.172 27.584770 41.638896
## 2023.172 27.712115 41.833654
## 2023.172 27.838855 42.027487
## 2023.172 27.964999 42.220407
## 2023.173 28.090555 42.412427
## 2023.173 28.215530 42.603561
## 2023.173 28.339934 42.793820
## 2023.173 28.463773 42.983216
## 2023.173 28.587056 43.171760
## 2023.173 28.709789 43.359465
## 2023.173 28.831981 43.546341
## 2023.173 28.953637 43.732399
## 2023.173 29.074766 43.917649
## 2023.174 29.195374 44.102103
## 2023.174 29.315468 44.285770
## 2023.174 29.435053 44.468661
## 2023.174 29.554137 44.650784
## 2023.174 29.672726 44.832150
## 2023.174 29.790826 45.012769
## 2023.174 29.908443 45.192648
## 2023.174 30.025583 45.371798
## 2023.174 30.142251 45.550226
## 2023.174 30.258453 45.727943
## 2023.175 30.374196 45.904955
## 2023.175 30.489483 46.081272
## 2023.175 30.604321 46.256901
## 2023.175 30.718714 46.431851
## 2023.175 30.832669 46.606129
## 2023.175 30.946189 46.779744
## 2023.175 31.059280 46.952701
## 2023.175 31.171947 47.125010
## 2023.175 31.284194 47.296677
## 2023.176 31.396026 47.467709
## 2023.176 31.507447 47.638114
## 2023.176 31.618463 47.807898
## 2023.176 31.729077 47.977068
## 2023.176 31.839294 48.145630
## 2023.176 31.949118 48.313591
## 2023.176 32.058553 48.480958
## 2023.176 32.167604 48.647736
## 2023.176 32.276273 48.813932
## 2023.176 32.384566 48.979552
## 2023.177 32.492487 49.144602
## 2023.177 32.600038 49.309087
## 2023.177 32.707224 49.473014
## 2023.177 32.814049 49.636388
## 2023.177 32.920515 49.799215
## 2023.177 33.026628 49.961500
## 2023.177 33.132389 50.123248
## 2023.177 33.237803 50.284465
## 2023.177 33.342873 50.445156
## 2023.178 33.447603 50.605326
## 2023.178 33.551995 50.764980
## 2023.178 33.656054 50.924124
## 2023.178 33.759781 51.082761
## 2023.178 33.863180 51.240897
## 2023.178 33.966255 51.398536
## 2023.178 34.069009 51.555684
## 2023.178 34.171443 51.712344
## 2023.178 34.273562 51.868522
## 2023.178 34.375368 52.024221
## 2023.179 34.476865 52.179446
## 2023.179 34.578054 52.334201
## 2023.179 34.678938 52.488491
## 2023.179 34.779522 52.642320
## 2023.179 34.879806 52.795691
## 2023.179 34.979794 52.948609
## 2023.179 35.079488 53.101079head(predict_winddir)## $method
## [1] "ARIMA(4,1,2)"
##
## $model
## Series: data_winddir
## ARIMA(4,1,2)
##
## Coefficients:
## ar1 ar2 ar3 ar4 ma1 ma2
## 1.1431 -0.0845 -0.0080 -0.1511 -1.8333 0.8361
## s.e. 0.0182 0.0163 0.0158 0.0105 0.0153 0.0155
##
## sigma^2 = 12752: log likelihood = -58400.78
## AIC=116815.6 AICc=116815.6 BIC=116865.7
##
## $level
## [1] 80 95
##
## $mean
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## [1] 56.47741 77.55992 96.88720 116.74816 129.11703 138.23816 144.54077
## [8] 147.87510 149.21240 149.03088 147.73147 145.74695 143.38758 140.89601
## [15] 138.45938 136.20322 134.20643 132.51040 131.12653 130.04474 129.24030
## [22] 128.67944 128.32402 128.13499 128.07497 128.10990 128.21012 128.35078
## [29] 128.51189 128.67809 128.83820 128.98464 129.11284 129.22063 129.30766
## [36] 129.37488 129.42413 129.45777 129.47839 129.48856 129.49073 129.48710
## [43] 129.47958 129.46974 129.45882 129.44777 129.43729 129.42781 129.41960
## [50] 129.41277 129.40731 129.40315 129.40015 129.39814 129.39696 129.39644
## [57] 129.39641 129.39673 129.39728 129.39796 129.39869 129.39942 129.40011
## [64] 129.40072 129.40125 129.40168 129.40202 129.40228 129.40246 129.40258
## [71] 129.40264 129.40267 129.40266 129.40264 129.40260 129.40255 129.40250
## [78] 129.40245 129.40241 129.40237 129.40234 129.40231 129.40229 129.40227
## [85] 129.40226 129.40225 129.40225 129.40225 129.40225 129.40225 129.40225
## [92] 129.40226 129.40226 129.40226 129.40227 129.40227 129.40227 129.40227
## [99] 129.40227 129.40228 129.40228 129.40228 129.40228 129.40228 129.40228
## [106] 129.40228 129.40228 129.40228 129.40228 129.40228 129.40228 129.40228
## [113] 129.40228 129.40228 129.40228 129.40228 129.40228 129.40228 129.40228
## [120] 129.40228 129.40228 129.40228 129.40228 129.40228 129.40228 129.40228
## [127] 129.40228 129.40228 129.40228 129.40228 129.40228 129.40228 129.40228
## [134] 129.40228 129.40228 129.40228 129.40228 129.40228 129.40228 129.40228
## [141] 129.40228 129.40228 129.40228 129.40228 129.40228 129.40228 129.40228
## [148] 129.40228 129.40228 129.40228 129.40228 129.40228 129.40228 129.40228
## [155] 129.40228 129.40228 129.40228 129.40228 129.40228 129.40228 129.40228
## [162] 129.40228 129.40228 129.40228 129.40228 129.40228 129.40228 129.40228
##
## $lower
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## 80% 95%
## 2023.162 -88.24404 -164.8550
## 2023.162 -73.94871 -154.1525
## 2023.162 -59.66903 -142.5449
## 2023.162 -44.97149 -130.5807
## 2023.162 -33.98510 -120.3261
## 2023.162 -25.47358 -112.1373
## 2023.162 -19.38986 -106.1695
## 2023.162 -16.07615 -102.8667
## 2023.162 -14.74015 -101.5314
## 2023.163 -14.94487 -101.7484
## 2023.163 -16.29287 -103.1221
## 2023.163 -18.33406 -105.1933
## 2023.163 -20.74378 -107.6297
## 2023.163 -23.27113 -110.1760
## 2023.163 -25.72727 -112.6424
## 2023.163 -27.99039 -114.9092
## 2023.163 -29.98785 -116.9071
## 2023.163 -31.68476 -118.6044
## 2023.164 -33.07508 -119.9982
## 2023.164 -34.17236 -121.1036
## 2023.164 -35.00292 -121.9480
## 2023.164 -35.60044 -122.5650
## 2023.164 -36.00188 -122.9908
## 2023.164 -36.24441 -123.2616
## 2023.164 -36.36336 -123.4117
## 2023.164 -36.39075 -123.4721
## 2023.164 -36.35453 -123.4698
## 2023.164 -36.27819 -123.4275
## 2023.165 -36.18069 -123.3637
## 2023.165 -36.07675 -123.2927
## 2023.165 -35.97716 -123.2251
## 2023.165 -35.88937 -123.1684
## 2023.165 -35.81797 -123.1271
## 2023.165 -35.76524 -123.1035
## 2023.165 -35.73174 -123.0983
## 2023.165 -35.71673 -123.1109
## 2023.165 -35.71860 -123.1399
## 2023.166 -35.73523 -123.1831
## 2023.166 -35.76423 -123.2384
## 2023.166 -35.80321 -123.3034
## 2023.166 -35.84987 -123.3759
## 2023.166 -35.90214 -123.4539
## 2023.166 -35.95822 -123.5357
## 2023.166 -36.01660 -123.6198
## 2023.166 -36.07609 -123.7050
## 2023.166 -36.13578 -123.7904
## 2023.166 -36.19500 -123.8754
## 2023.167 -36.25331 -123.9596
## 2023.167 -36.31045 -124.0426
## 2023.167 -36.36630 -124.1244
## 2023.167 -36.42085 -124.2050
## 2023.167 -36.47418 -124.2843
## 2023.167 -36.52639 -124.3626
## 2023.167 -36.57765 -124.4399
## 2023.167 -36.62811 -124.5165
## 2023.167 -36.67792 -124.5924
## 2023.168 -36.72724 -124.6678
## 2023.168 -36.77621 -124.7429
## 2023.168 -36.82494 -124.8177
## 2023.168 -36.87352 -124.8923
## 2023.168 -36.92202 -124.9669
## 2023.168 -36.97051 -125.0414
## 2023.168 -37.01903 -125.1160
## 2023.168 -37.06759 -125.1906
## 2023.168 -37.11622 -125.2652
## 2023.168 -37.16493 -125.3400
## 2023.169 -37.21370 -125.4147
## 2023.169 -37.26254 -125.4896
## 2023.169 -37.31143 -125.5644
## 2023.169 -37.36037 -125.6393
## 2023.169 -37.40934 -125.7143
## 2023.169 -37.45834 -125.7892
## 2023.169 -37.50736 -125.8642
## 2023.169 -37.55638 -125.9391
## 2023.169 -37.60540 -126.0141
## 2023.170 -37.65441 -126.0890
## 2023.170 -37.70341 -126.1639
## 2023.170 -37.75240 -126.2388
## 2023.170 -37.80136 -126.3137
## 2023.170 -37.85031 -126.3885
## 2023.170 -37.89924 -126.4634
## 2023.170 -37.94815 -126.5381
## 2023.170 -37.99704 -126.6129
## 2023.170 -38.04591 -126.6876
## 2023.170 -38.09477 -126.7623
## 2023.171 -38.14360 -126.8370
## 2023.171 -38.19242 -126.9117
## 2023.171 -38.24122 -126.9863
## 2023.171 -38.29000 -127.0609
## 2023.171 -38.33877 -127.1355
## 2023.171 -38.38752 -127.2101
## 2023.171 -38.43626 -127.2846
## 2023.171 -38.48498 -127.3591
## 2023.171 -38.53369 -127.4336
## 2023.172 -38.58239 -127.5081
## 2023.172 -38.63107 -127.5826
## 2023.172 -38.67974 -127.6570
## 2023.172 -38.72840 -127.7314
## 2023.172 -38.77704 -127.8058
## 2023.172 -38.82567 -127.8802
## 2023.172 -38.87428 -127.9545
## 2023.172 -38.92288 -128.0288
## 2023.172 -38.97147 -128.1031
## 2023.172 -39.02004 -128.1774
## 2023.173 -39.06860 -128.2517
## 2023.173 -39.11714 -128.3259
## 2023.173 -39.16567 -128.4002
## 2023.173 -39.21419 -128.4744
## 2023.173 -39.26269 -128.5485
## 2023.173 -39.31118 -128.6227
## 2023.173 -39.35966 -128.6968
## 2023.173 -39.40812 -128.7709
## 2023.173 -39.45657 -128.8450
## 2023.174 -39.50500 -128.9191
## 2023.174 -39.55342 -128.9932
## 2023.174 -39.60182 -129.0672
## 2023.174 -39.65022 -129.1412
## 2023.174 -39.69859 -129.2152
## 2023.174 -39.74696 -129.2892
## 2023.174 -39.79531 -129.3631
## 2023.174 -39.84364 -129.4370
## 2023.174 -39.89197 -129.5109
## 2023.174 -39.94027 -129.5848
## 2023.175 -39.98857 -129.6587
## 2023.175 -40.03685 -129.7325
## 2023.175 -40.08512 -129.8063
## 2023.175 -40.13337 -129.8801
## 2023.175 -40.18161 -129.9539
## 2023.175 -40.22984 -130.0277
## 2023.175 -40.27805 -130.1014
## 2023.175 -40.32625 -130.1751
## 2023.175 -40.37443 -130.2488
## 2023.176 -40.42260 -130.3225
## 2023.176 -40.47076 -130.3961
## 2023.176 -40.51891 -130.4697
## 2023.176 -40.56704 -130.5434
## 2023.176 -40.61515 -130.6169
## 2023.176 -40.66326 -130.6905
## 2023.176 -40.71135 -130.7641
## 2023.176 -40.75942 -130.8376
## 2023.176 -40.80748 -130.9111
## 2023.176 -40.85553 -130.9846
## 2023.177 -40.90357 -131.0580
## 2023.177 -40.95159 -131.1315
## 2023.177 -40.99960 -131.2049
## 2023.177 -41.04759 -131.2783
## 2023.177 -41.09557 -131.3517
## 2023.177 -41.14354 -131.4250
## 2023.177 -41.19150 -131.4984
## 2023.177 -41.23944 -131.5717
## 2023.177 -41.28736 -131.6450
## 2023.178 -41.33528 -131.7183
## 2023.178 -41.38318 -131.7915
## 2023.178 -41.43107 -131.8648
## 2023.178 -41.47894 -131.9380
## 2023.178 -41.52680 -132.0112
## 2023.178 -41.57465 -132.0844
## 2023.178 -41.62248 -132.1575
## 2023.178 -41.67030 -132.2306
## 2023.178 -41.71811 -132.3038
## 2023.178 -41.76590 -132.3769
## 2023.179 -41.81368 -132.4499
## 2023.179 -41.86145 -132.5230
## 2023.179 -41.90920 -132.5960
## 2023.179 -41.95694 -132.6690
## 2023.179 -42.00466 -132.7420
## 2023.179 -42.05238 -132.8150
## 2023.179 -42.10008 -132.8879
##
## $upper
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## 80% 95%
## 2023.162 201.1989 277.8098
## 2023.162 229.0685 309.2724
## 2023.162 253.4434 336.3193
## 2023.162 278.4678 364.0770
## 2023.162 292.2192 378.5602
## 2023.162 301.9499 388.6136
## 2023.162 308.4714 395.2510
## 2023.162 311.8264 398.6169
## 2023.162 313.1649 399.9562
## 2023.163 313.0066 399.8102
## 2023.163 311.7558 398.5851
## 2023.163 309.8280 396.6872
## 2023.163 307.5189 394.4048
## 2023.163 305.0632 391.9680
## 2023.163 302.6460 389.5612
## 2023.163 300.3968 387.3157
## 2023.163 298.4007 385.3199
## 2023.163 296.7056 383.6252
## 2023.164 295.3281 382.2512
## 2023.164 294.2618 381.1931
## 2023.164 293.4835 380.4286
## 2023.164 292.9593 379.9238
## 2023.164 292.6499 379.6388
## 2023.164 292.5144 379.5316
## 2023.164 292.5133 379.5617
## 2023.164 292.6106 379.6919
## 2023.164 292.7748 379.8901
## 2023.164 292.9797 380.1291
## 2023.165 293.2045 380.3874
## 2023.165 293.4329 380.6489
## 2023.165 293.6536 380.9015
## 2023.165 293.8587 381.1377
## 2023.165 294.0437 381.3527
## 2023.165 294.2065 381.5448
## 2023.165 294.3471 381.7136
## 2023.165 294.4665 381.8607
## 2023.165 294.5669 381.9881
## 2023.166 294.6508 382.0987
## 2023.166 294.7210 382.1952
## 2023.166 294.7803 382.2805
## 2023.166 294.8313 382.3574
## 2023.166 294.8763 382.4281
## 2023.166 294.9174 382.4949
## 2023.166 294.9561 382.5592
## 2023.166 294.9937 382.6226
## 2023.166 295.0313 382.6860
## 2023.166 295.0696 382.7500
## 2023.167 295.1089 382.8152
## 2023.167 295.1497 382.8818
## 2023.167 295.1918 382.9500
## 2023.167 295.2355 383.0196
## 2023.167 295.2805 383.0906
## 2023.167 295.3267 383.1629
## 2023.167 295.3739 383.2362
## 2023.167 295.4220 383.3104
## 2023.167 295.4708 383.3853
## 2023.168 295.5201 383.4606
## 2023.168 295.5697 383.5363
## 2023.168 295.6195 383.6122
## 2023.168 295.6694 383.6882
## 2023.168 295.7194 383.7643
## 2023.168 295.7694 383.8403
## 2023.168 295.8192 383.9162
## 2023.168 295.8690 383.9920
## 2023.168 295.9187 384.0677
## 2023.168 295.9683 384.1433
## 2023.169 296.0177 384.2188
## 2023.169 296.0671 384.2941
## 2023.169 296.1163 384.3693
## 2023.169 296.1655 384.4445
## 2023.169 296.2146 384.5196
## 2023.169 296.2637 384.5946
## 2023.169 296.3127 384.6695
## 2023.169 296.3617 384.7444
## 2023.169 296.4106 384.8193
## 2023.170 296.4595 384.8941
## 2023.170 296.5084 384.9689
## 2023.170 296.5573 385.0437
## 2023.170 296.6062 385.1185
## 2023.170 296.6551 385.1933
## 2023.170 296.7039 385.2680
## 2023.170 296.7528 385.3428
## 2023.170 296.8016 385.4175
## 2023.170 296.8505 385.4922
## 2023.170 296.8993 385.5669
## 2023.171 296.9481 385.6415
## 2023.171 296.9969 385.7162
## 2023.171 297.0457 385.7908
## 2023.171 297.0945 385.8654
## 2023.171 297.1433 385.9400
## 2023.171 297.1920 386.0146
## 2023.171 297.2408 386.0891
## 2023.171 297.2895 386.1636
## 2023.171 297.3382 386.2381
## 2023.172 297.3869 386.3126
## 2023.172 297.4356 386.3871
## 2023.172 297.4843 386.4615
## 2023.172 297.5329 386.5359
## 2023.172 297.5816 386.6103
## 2023.172 297.6302 386.6847
## 2023.172 297.6788 386.7591
## 2023.172 297.7274 386.8334
## 2023.172 297.7760 386.9077
## 2023.172 297.8246 386.9820
## 2023.173 297.8732 387.0562
## 2023.173 297.9217 387.1305
## 2023.173 297.9702 387.2047
## 2023.173 298.0187 387.2789
## 2023.173 298.0672 387.3531
## 2023.173 298.1157 387.4272
## 2023.173 298.1642 387.5014
## 2023.173 298.2127 387.5755
## 2023.173 298.2611 387.6496
## 2023.174 298.3096 387.7237
## 2023.174 298.3580 387.7977
## 2023.174 298.4064 387.8717
## 2023.174 298.4548 387.9457
## 2023.174 298.5031 388.0197
## 2023.174 298.5515 388.0937
## 2023.174 298.5999 388.1676
## 2023.174 298.6482 388.2416
## 2023.174 298.6965 388.3155
## 2023.174 298.7448 388.3894
## 2023.175 298.7931 388.4632
## 2023.175 298.8414 388.5371
## 2023.175 298.8897 388.6109
## 2023.175 298.9379 388.6847
## 2023.175 298.9862 388.7584
## 2023.175 299.0344 388.8322
## 2023.175 299.0826 388.9059
## 2023.175 299.1308 388.9797
## 2023.175 299.1790 389.0533
## 2023.176 299.2272 389.1270
## 2023.176 299.2753 389.2007
## 2023.176 299.3235 389.2743
## 2023.176 299.3716 389.3479
## 2023.176 299.4197 389.4215
## 2023.176 299.4678 389.4951
## 2023.176 299.5159 389.5686
## 2023.176 299.5640 389.6421
## 2023.176 299.6120 389.7156
## 2023.176 299.6601 389.7891
## 2023.177 299.7081 389.8626
## 2023.177 299.7561 389.9360
## 2023.177 299.8041 390.0095
## 2023.177 299.8521 390.0829
## 2023.177 299.9001 390.1562
## 2023.177 299.9481 390.2296
## 2023.177 299.9960 390.3029
## 2023.177 300.0440 390.3763
## 2023.177 300.0919 390.4496
## 2023.178 300.1398 390.5228
## 2023.178 300.1877 390.5961
## 2023.178 300.2356 390.6693
## 2023.178 300.2835 390.7425
## 2023.178 300.3313 390.8157
## 2023.178 300.3792 390.8889
## 2023.178 300.4270 390.9621
## 2023.178 300.4748 391.0352
## 2023.178 300.5227 391.1083
## 2023.178 300.5704 391.1814
## 2023.179 300.6182 391.2545
## 2023.179 300.6660 391.3275
## 2023.179 300.7137 391.4006
## 2023.179 300.7615 391.4736
## 2023.179 300.8092 391.5466
## 2023.179 300.8569 391.6195
## 2023.179 300.9046 391.6925head(predict_sealevelpressure)## $method
## [1] "ARIMA(5,1,1)"
##
## $model
## Series: data_sealevelpressure
## ARIMA(5,1,1)
##
## Coefficients:
## ar1 ar2 ar3 ar4 ar5 ma1
## 0.6614 0.1230 -0.0645 -0.2026 -0.2223 -0.5825
## s.e. 0.0160 0.0124 0.0127 0.0119 0.0124 0.0142
##
## sigma^2 = 0.2953: log likelihood = -7684.41
## AIC=15382.82 AICc=15382.84 BIC=15432.94
##
## $level
## [1] 80 95
##
## $mean
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## [1] 1009.738 1009.299 1008.765 1008.155 1007.745 1007.580 1007.665 1007.971
## [9] 1008.413 1008.861 1009.212 1009.390 1009.364 1009.158 1008.835 1008.484
## [17] 1008.192 1008.023 1008.010 1008.142 1008.376 1008.647 1008.886 1009.039
## [25] 1009.076 1008.996 1008.829 1008.623 1008.429 1008.295 1008.247 1008.290
## [33] 1008.406 1008.562 1008.716 1008.832 1008.884 1008.865 1008.786 1008.670
## [41] 1008.549 1008.452 1008.400 1008.403 1008.455 1008.540 1008.634 1008.715
## [49] 1008.763 1008.769 1008.737 1008.676 1008.603 1008.538 1008.494 1008.482
## [57] 1008.501 1008.545 1008.600 1008.652 1008.690 1008.705 1008.695 1008.665
## [65] 1008.624 1008.582 1008.550 1008.534 1008.538 1008.558 1008.589 1008.621
## [73] 1008.648 1008.663 1008.663 1008.650 1008.628 1008.603 1008.581 1008.567
## [81] 1008.564 1008.572 1008.588 1008.608 1008.626 1008.638 1008.642 1008.637
## [89] 1008.626 1008.611 1008.597 1008.587 1008.582 1008.584 1008.592 1008.603
## [97] 1008.614 1008.623 1008.628 1008.627 1008.622 1008.614 1008.605 1008.598
## [105] 1008.593 1008.593 1008.596 1008.602 1008.609 1008.615 1008.619 1008.620
## [113] 1008.618 1008.614 1008.608 1008.604 1008.600 1008.599 1008.600 1008.603
## [121] 1008.607 1008.611 1008.614 1008.615 1008.614 1008.612 1008.610 1008.607
## [129] 1008.604 1008.603 1008.603 1008.604 1008.606 1008.609 1008.611 1008.612
## [137] 1008.612 1008.611 1008.610 1008.608 1008.606 1008.605 1008.605 1008.605
## [145] 1008.606 1008.608 1008.609 1008.610 1008.610 1008.610 1008.609 1008.608
## [153] 1008.607 1008.607 1008.606 1008.606 1008.607 1008.607 1008.608 1008.609
## [161] 1008.609 1008.609 1008.609 1008.608 1008.608 1008.607 1008.607 1008.607
##
## $lower
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## 80% 95%
## 2023.162 1009.042 1008.673
## 2023.162 1008.275 1007.732
## 2023.162 1007.419 1006.706
## 2023.162 1006.527 1005.665
## 2023.162 1005.924 1004.960
## 2023.162 1005.670 1004.658
## 2023.162 1005.720 1004.690
## 2023.162 1006.017 1004.983
## 2023.162 1006.458 1005.424
## 2023.163 1006.907 1005.873
## 2023.163 1007.256 1006.220
## 2023.163 1007.419 1006.376
## 2023.163 1007.352 1006.286
## 2023.163 1007.066 1005.959
## 2023.163 1006.636 1005.472
## 2023.163 1006.172 1004.948
## 2023.163 1005.783 1004.508
## 2023.163 1005.547 1004.236
## 2023.164 1005.494 1004.162
## 2023.164 1005.606 1004.264
## 2023.164 1005.830 1004.483
## 2023.164 1006.095 1004.744
## 2023.164 1006.327 1004.972
## 2023.164 1006.466 1005.103
## 2023.164 1006.476 1005.100
## 2023.164 1006.355 1004.957
## 2023.164 1006.133 1004.705
## 2023.164 1005.863 1004.402
## 2023.165 1005.608 1004.115
## 2023.165 1005.423 1003.903
## 2023.165 1005.338 1003.798
## 2023.165 1005.355 1003.802
## 2023.165 1005.454 1003.891
## 2023.165 1005.596 1004.027
## 2023.165 1005.738 1004.161
## 2023.165 1005.837 1004.252
## 2023.165 1005.868 1004.272
## 2023.166 1005.820 1004.209
## 2023.166 1005.705 1004.073
## 2023.166 1005.547 1003.894
## 2023.166 1005.383 1003.707
## 2023.166 1005.247 1003.550
## 2023.166 1005.162 1003.448
## 2023.166 1005.138 1003.410
## 2023.166 1005.169 1003.429
## 2023.166 1005.236 1003.487
## 2023.166 1005.314 1003.556
## 2023.167 1005.377 1003.610
## 2023.167 1005.405 1003.627
## 2023.167 1005.388 1003.597
## 2023.167 1005.327 1003.522
## 2023.167 1005.235 1003.413
## 2023.167 1005.130 1003.291
## 2023.167 1005.032 1003.177
## 2023.167 1004.960 1003.089
## 2023.167 1004.922 1003.038
## 2023.168 1004.919 1003.023
## 2023.168 1004.942 1003.035
## 2023.168 1004.979 1003.062
## 2023.168 1005.013 1003.086
## 2023.168 1005.031 1003.094
## 2023.168 1005.024 1003.076
## 2023.168 1004.991 1003.030
## 2023.168 1004.935 1002.961
## 2023.168 1004.867 1002.879
## 2023.168 1004.799 1002.797
## 2023.169 1004.741 1002.725
## 2023.169 1004.702 1002.673
## 2023.169 1004.683 1002.643
## 2023.169 1004.683 1002.632
## 2023.169 1004.695 1002.633
## 2023.169 1004.708 1002.637
## 2023.169 1004.716 1002.634
## 2023.169 1004.711 1002.618
## 2023.169 1004.690 1002.586
## 2023.170 1004.654 1002.539
## 2023.170 1004.609 1002.481
## 2023.170 1004.560 1002.420
## 2023.170 1004.515 1002.363
## 2023.170 1004.479 1002.315
## 2023.170 1004.456 1002.280
## 2023.170 1004.444 1002.258
## 2023.170 1004.440 1002.244
## 2023.170 1004.441 1002.235
## 2023.170 1004.440 1002.224
## 2023.171 1004.433 1002.207
## 2023.171 1004.417 1002.180
## 2023.171 1004.392 1002.145
## 2023.171 1004.360 1002.102
## 2023.171 1004.324 1002.055
## 2023.171 1004.289 1002.008
## 2023.171 1004.258 1001.966
## 2023.171 1004.233 1001.931
## 2023.171 1004.216 1001.904
## 2023.172 1004.205 1001.882
## 2023.172 1004.197 1001.865
## 2023.172 1004.190 1001.848
## 2023.172 1004.181 1001.829
## 2023.172 1004.166 1001.805
## 2023.172 1004.147 1001.775
## 2023.172 1004.122 1001.741
## 2023.172 1004.095 1001.703
## 2023.172 1004.067 1001.664
## 2023.172 1004.040 1001.627
## 2023.173 1004.017 1001.594
## 2023.173 1003.998 1001.565
## 2023.173 1003.983 1001.541
## 2023.173 1003.971 1001.519
## 2023.173 1003.960 1001.498
## 2023.173 1003.948 1001.477
## 2023.173 1003.934 1001.454
## 2023.173 1003.917 1001.427
## 2023.173 1003.897 1001.397
## 2023.174 1003.874 1001.365
## 2023.174 1003.851 1001.332
## 2023.174 1003.828 1001.299
## 2023.174 1003.806 1001.268
## 2023.174 1003.787 1001.240
## 2023.174 1003.771 1001.214
## 2023.174 1003.756 1001.190
## 2023.174 1003.743 1001.168
## 2023.174 1003.730 1001.146
## 2023.174 1003.715 1001.122
## 2023.175 1003.699 1001.097
## 2023.175 1003.682 1001.070
## 2023.175 1003.662 1001.042
## 2023.175 1003.642 1001.012
## 2023.175 1003.622 1000.983
## 2023.175 1003.602 1000.954
## 2023.175 1003.584 1000.927
## 2023.175 1003.567 1000.901
## 2023.175 1003.551 1000.877
## 2023.176 1003.537 1000.853
## 2023.176 1003.523 1000.830
## 2023.176 1003.508 1000.807
## 2023.176 1003.493 1000.783
## 2023.176 1003.476 1000.758
## 2023.176 1003.459 1000.732
## 2023.176 1003.441 1000.705
## 2023.176 1003.423 1000.678
## 2023.176 1003.404 1000.651
## 2023.176 1003.387 1000.625
## 2023.177 1003.370 1000.599
## 2023.177 1003.355 1000.575
## 2023.177 1003.340 1000.551
## 2023.177 1003.325 1000.528
## 2023.177 1003.310 1000.505
## 2023.177 1003.295 1000.482
## 2023.177 1003.280 1000.458
## 2023.177 1003.263 1000.433
## 2023.177 1003.247 1000.408
## 2023.178 1003.230 1000.383
## 2023.178 1003.213 1000.357
## 2023.178 1003.196 1000.332
## 2023.178 1003.180 1000.308
## 2023.178 1003.165 1000.284
## 2023.178 1003.150 1000.261
## 2023.178 1003.135 1000.238
## 2023.178 1003.120 1000.215
## 2023.178 1003.105 1000.192
## 2023.178 1003.090 1000.169
## 2023.179 1003.075 1000.145
## 2023.179 1003.059 1000.122
## 2023.179 1003.043 1000.098
## 2023.179 1003.028 1000.074
## 2023.179 1003.012 1000.050
## 2023.179 1002.996 1000.026
## 2023.179 1002.981 1000.003
##
## $upper
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## 80% 95%
## 2023.162 1010.435 1010.803
## 2023.162 1010.323 1010.866
## 2023.162 1010.111 1010.824
## 2023.162 1009.783 1010.645
## 2023.162 1009.565 1010.529
## 2023.162 1009.490 1010.501
## 2023.162 1009.611 1010.641
## 2023.162 1009.924 1010.959
## 2023.162 1010.367 1011.401
## 2023.163 1010.816 1011.850
## 2023.163 1011.169 1012.205
## 2023.163 1011.361 1012.404
## 2023.163 1011.377 1012.442
## 2023.163 1011.249 1012.356
## 2023.163 1011.034 1012.198
## 2023.163 1010.796 1012.020
## 2023.163 1010.600 1011.875
## 2023.163 1010.500 1011.810
## 2023.164 1010.526 1011.857
## 2023.164 1010.677 1012.020
## 2023.164 1010.921 1012.268
## 2023.164 1011.198 1012.549
## 2023.164 1011.446 1012.801
## 2023.164 1011.613 1012.975
## 2023.164 1011.675 1013.051
## 2023.164 1011.637 1013.035
## 2023.164 1011.526 1012.954
## 2023.164 1011.383 1012.844
## 2023.165 1011.250 1012.744
## 2023.165 1011.166 1012.686
## 2023.165 1011.156 1012.696
## 2023.165 1011.224 1012.778
## 2023.165 1011.358 1012.921
## 2023.165 1011.527 1013.097
## 2023.165 1011.694 1013.271
## 2023.165 1011.826 1013.411
## 2023.165 1011.899 1013.496
## 2023.166 1011.910 1013.521
## 2023.166 1011.867 1013.498
## 2023.166 1011.793 1013.446
## 2023.166 1011.714 1013.390
## 2023.166 1011.656 1013.353
## 2023.166 1011.638 1013.352
## 2023.166 1011.668 1013.396
## 2023.166 1011.741 1013.481
## 2023.166 1011.844 1013.593
## 2023.166 1011.955 1013.712
## 2023.167 1012.052 1013.819
## 2023.167 1012.121 1013.898
## 2023.167 1012.151 1013.942
## 2023.167 1012.147 1013.952
## 2023.167 1012.117 1013.938
## 2023.167 1012.077 1013.915
## 2023.167 1012.043 1013.898
## 2023.167 1012.029 1013.900
## 2023.167 1012.042 1013.927
## 2023.168 1012.084 1013.980
## 2023.168 1012.147 1014.054
## 2023.168 1012.221 1014.137
## 2023.168 1012.292 1014.218
## 2023.168 1012.349 1014.286
## 2023.168 1012.385 1014.333
## 2023.168 1012.399 1014.360
## 2023.168 1012.394 1014.369
## 2023.168 1012.380 1014.368
## 2023.168 1012.365 1014.367
## 2023.169 1012.358 1014.375
## 2023.169 1012.367 1014.396
## 2023.169 1012.393 1014.433
## 2023.169 1012.433 1014.484
## 2023.169 1012.483 1014.544
## 2023.169 1012.535 1014.606
## 2023.169 1012.581 1014.662
## 2023.169 1012.616 1014.708
## 2023.169 1012.637 1014.741
## 2023.170 1012.646 1014.762
## 2023.170 1012.647 1014.775
## 2023.170 1012.645 1014.785
## 2023.170 1012.646 1014.798
## 2023.170 1012.655 1014.819
## 2023.170 1012.673 1014.848
## 2023.170 1012.701 1014.887
## 2023.170 1012.737 1014.933
## 2023.170 1012.775 1014.981
## 2023.170 1012.812 1015.027
## 2023.171 1012.843 1015.069
## 2023.171 1012.867 1015.103
## 2023.171 1012.882 1015.130
## 2023.171 1012.892 1015.150
## 2023.171 1012.898 1015.168
## 2023.171 1012.905 1015.186
## 2023.171 1012.915 1015.207
## 2023.171 1012.931 1015.233
## 2023.171 1012.952 1015.265
## 2023.172 1012.979 1015.301
## 2023.172 1013.008 1015.341
## 2023.172 1013.038 1015.380
## 2023.172 1013.065 1015.417
## 2023.172 1013.089 1015.450
## 2023.172 1013.107 1015.479
## 2023.172 1013.121 1015.503
## 2023.172 1013.133 1015.525
## 2023.172 1013.143 1015.546
## 2023.172 1013.155 1015.568
## 2023.173 1013.170 1015.593
## 2023.173 1013.188 1015.621
## 2023.173 1013.210 1015.652
## 2023.173 1013.234 1015.685
## 2023.173 1013.258 1015.719
## 2023.173 1013.282 1015.753
## 2023.173 1013.304 1015.784
## 2023.173 1013.323 1015.812
## 2023.173 1013.339 1015.838
## 2023.174 1013.353 1015.862
## 2023.174 1013.366 1015.885
## 2023.174 1013.379 1015.908
## 2023.174 1013.394 1015.932
## 2023.174 1013.411 1015.958
## 2023.174 1013.429 1015.986
## 2023.174 1013.450 1016.015
## 2023.174 1013.471 1016.045
## 2023.174 1013.492 1016.076
## 2023.174 1013.512 1016.105
## 2023.175 1013.530 1016.132
## 2023.175 1013.547 1016.158
## 2023.175 1013.563 1016.183
## 2023.175 1013.577 1016.207
## 2023.175 1013.591 1016.230
## 2023.175 1013.606 1016.254
## 2023.175 1013.622 1016.279
## 2023.175 1013.639 1016.305
## 2023.175 1013.657 1016.332
## 2023.176 1013.676 1016.359
## 2023.176 1013.695 1016.387
## 2023.176 1013.713 1016.414
## 2023.176 1013.731 1016.441
## 2023.176 1013.748 1016.466
## 2023.176 1013.763 1016.491
## 2023.176 1013.778 1016.515
## 2023.176 1013.793 1016.538
## 2023.176 1013.808 1016.562
## 2023.176 1013.823 1016.586
## 2023.177 1013.839 1016.610
## 2023.177 1013.856 1016.636
## 2023.177 1013.873 1016.661
## 2023.177 1013.891 1016.687
## 2023.177 1013.908 1016.713
## 2023.177 1013.925 1016.738
## 2023.177 1013.941 1016.763
## 2023.177 1013.957 1016.787
## 2023.177 1013.972 1016.811
## 2023.178 1013.987 1016.834
## 2023.178 1014.002 1016.857
## 2023.178 1014.017 1016.881
## 2023.178 1014.032 1016.904
## 2023.178 1014.048 1016.928
## 2023.178 1014.064 1016.953
## 2023.178 1014.080 1016.977
## 2023.178 1014.096 1017.002
## 2023.178 1014.112 1017.026
## 2023.178 1014.128 1017.050
## 2023.179 1014.144 1017.073
## 2023.179 1014.159 1017.097
## 2023.179 1014.173 1017.119
## 2023.179 1014.188 1017.142
## 2023.179 1014.203 1017.165
## 2023.179 1014.218 1017.188
## 2023.179 1014.233 1017.211head(predict_cloudcover)## $method
## [1] "ARIMA(2,1,2)"
##
## $model
## Series: data_cloudcover
## ARIMA(2,1,2)
##
## Coefficients:
## ar1 ar2 ma1 ma2
## -0.9751 -0.9528 0.4660 0.4498
## s.e. 0.0050 0.0077 0.0419 0.0435
##
## sigma^2 = 107.9: log likelihood = -35726.91
## AIC=71463.81 AICc=71463.82 BIC=71499.61
##
## $level
## [1] 80 95
##
## $mean
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## [1] 55.35106 47.42349 57.01075 55.21536 47.83140 56.74228 55.08850 48.21091
## [9] 56.49309 54.96990 48.56398 56.26180 54.85902 48.89247 56.04713 54.75538
## [17] 49.19808 55.84788 54.65849 49.48240 55.66295 54.56792 49.74692 55.49130
## [25] 54.48326 49.99301 55.33200 54.40413 50.22196 55.18414 54.33016 50.43496
## [33] 55.04691 54.26103 50.63312 54.91955 54.19642 50.81748 54.80134 54.13603
## [41] 50.98899 54.69163 54.07960 51.14855 54.58981 54.02685 51.29699 54.49532
## [49] 53.97756 51.43509 54.40762 53.93150 51.56357 54.32623 53.88846 51.68309
## [57] 54.25069 53.84823 51.79429 54.18059 53.81064 51.89773 54.11553 53.77552
## [65] 51.99397 54.05515 53.74271 52.08349 53.99912 53.71204 52.16678 53.94712
## [73] 53.68339 52.24426 53.89886 53.65662 52.31634 53.85408 53.63161 52.38340
## [81] 53.81252 53.60825 52.44578 53.77396 53.58642 52.50381 53.73817 53.56602
## [89] 52.55780 53.70496 53.54697 52.60802 53.67414 53.52917 52.65474 53.64554
## [97] 53.51254 52.69821 53.61900 53.49701 52.73864 53.59437 53.48250 52.77625
## [105] 53.57152 53.46895 52.81124 53.55031 53.45629 52.84379 53.53063 53.44447
## [113] 52.87407 53.51237 53.43342 52.90224 53.49543 53.42310 52.92844 53.47971
## [121] 53.41347 52.95282 53.46512 53.40447 52.97549 53.45158 53.39606 52.99659
## [129] 53.43902 53.38821 53.01621 53.42737 53.38088 53.03446 53.41655 53.37404
## [137] 53.05144 53.40652 53.36764 53.06723 53.39721 53.36167 53.08193 53.38857
## [145] 53.35610 53.09560 53.38056 53.35089 53.10831 53.37312 53.34603 53.12014
## [153] 53.36622 53.34148 53.13114 53.35982 53.33724 53.14137 53.35388 53.33329
## [161] 53.15089 53.34837 53.32959 53.15975 53.34326 53.32614 53.16798 53.33852
##
## $lower
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## 80% 95%
## 2023.162 42.0384647 34.9912045
## 2023.162 32.5934632 24.7429216
## 2023.162 40.8396992 32.2792604
## 2023.162 34.4727646 23.4922975
## 2023.162 26.0355552 14.4975313
## 2023.162 33.9690961 21.9137008
## 2023.162 29.0178933 15.2169487
## 2023.162 21.2577745 6.9896484
## 2023.162 28.7095608 14.0018484
## 2023.163 24.5398352 8.4311333
## 2023.163 17.3442997 0.8175981
## 2023.163 24.2942364 7.3716295
## 2023.163 20.6592616 2.5550013
## 2023.163 13.9627579 -4.5279137
## 2023.163 20.4233261 1.5652239
## 2023.163 17.1927076 -2.6917679
## 2023.163 10.9476513 -9.3009003
## 2023.163 16.9416833 -3.6540129
## 2023.164 14.0345879 -7.4704051
## 2023.164 8.2025683 -13.6496533
## 2023.164 13.7563530 -8.4276581
## 2023.164 11.1174595 -11.8838237
## 2023.164 5.6656994 -17.6694868
## 2023.164 10.8062903 -12.8485274
## 2023.164 8.3951183 -16.0024697
## 2023.164 3.2947646 -21.4257921
## 2023.164 8.0487917 -16.9814253
## 2023.164 5.8342746 -19.8770526
## 2023.165 1.0594565 -24.9655994
## 2023.165 5.4525709 -20.8737315
## 2023.165 3.4100128 -23.5454871
## 2023.165 -1.0627337 -28.3239647
## 2023.165 2.9938821 -24.5613269
## 2023.165 1.1031218 -27.0369769
## 2023.165 -3.0889938 -31.5277627
## 2023.165 0.6541937 -28.0721492
## 2023.165 -1.1015709 -30.3745599
## 2023.166 -5.0327465 -34.5980680
## 2023.166 -1.5812862 -31.4284456
## 2023.166 -3.2162865 -33.5767713
## 2023.166 -6.9046696 -37.5517201
## 2023.166 -3.7245866 -34.6482648
## 2023.166 -5.2510337 -36.6587728
## 2023.166 -8.7133915 -40.4023883
## 2023.166 -5.7856435 -37.7464797
## 2023.166 -7.2141255 -39.6331418
## 2023.166 -10.4659787 -43.1613204
## 2023.167 -7.7727749 -40.7355115
## 2023.167 -9.1125514 -42.5104407
## 2023.167 -12.1682868 -45.8378817
## 2023.167 -9.6930245 -43.6258542
## 2023.167 -10.9522511 -45.2996343
## 2023.167 -13.8252186 -48.4399517
## 2023.167 -11.5524175 -46.4264642
## 2023.167 -12.7383199 -48.0084041
## 2023.167 -15.4409176 -50.9742221
## 2023.168 -13.3561536 -49.1450539
## 2023.168 -14.4751657 -50.6433873
## 2023.168 -17.0189155 -53.4464242
## 2023.168 -15.1087558 -51.7883176
## 2023.168 -16.1666308 -53.2103627
## 2023.168 -18.5622455 -55.8615041
## 2023.168 -16.8141858 -54.3621080
## 2023.168 -17.8160871 -55.7143961
## 2023.168 -20.0735318 -58.2237609
## 2023.168 -18.4759363 -56.8715744
## 2023.169 -19.4265122 -58.1599562
## 2023.169 -21.5550597 -60.5369554
## 2023.169 -20.0971039 -59.3212747
## 2023.169 -21.0005506 -60.5510078
## 2023.169 -23.0088320 -62.8043980
## 2023.169 -21.6804483 -61.7152644
## 2023.169 -22.5405629 -62.8910868
## 2023.169 -24.4366140 -65.0290186
## 2023.169 -23.2284413 -64.0571699
## 2023.170 -24.0486666 -65.1833619
## 2023.170 -25.8399706 -67.2134247
## 2023.170 -24.7433058 -66.3502488
## 2023.170 -25.5267696 -67.4306859
## 2023.170 -27.2202963 -69.3599480
## 2023.170 -26.2270494 -68.5974392
## 2023.170 -26.9765978 -69.6356375
## 2023.170 -28.5788397 -71.4706837
## 2023.170 -27.6814921 -70.8014016
## 2023.170 -28.3997186 -71.8005568
## 2023.171 -29.9167247 -73.5475228
## 2023.171 -29.1082890 -72.9645536
## 2023.171 -29.7975603 -73.9275751
## 2023.171 -31.2349670 -75.5921791
## 2023.171 -30.5089508 -75.0890998
## 2023.171 -31.1714290 -76.0186401
## 2023.171 -32.5344887 -77.6062122
## 2023.171 -31.8848600 -77.1770563
## 2023.171 -32.5225224 -78.0755371
## 2023.172 -33.8161304 -79.5910465
## 2023.172 -33.2372853 -79.2302725
## 2023.172 -33.8519424 -80.0999076
## 2023.172 -35.0806613 -81.5479875
## 2023.172 -34.5673944 -81.2504497
## 2023.172 -35.1607050 -82.0932651
## 2023.172 -36.3287880 -83.4782361
## 2023.172 -35.8762642 -83.2391565
## 2023.172 -36.4497499 -84.0570085
## 2023.172 -37.5611618 -85.3829002
## 2023.173 -37.1648907 -85.1978431
## 2023.173 -37.7199478 -85.9924342
## 2023.173 -38.7783852 -87.2630053
## 2023.173 -38.4341966 -87.1278530
## 2023.173 -38.9721078 -87.9007460
## 2023.173 -39.9810171 -89.1195028
## 2023.173 -39.6850384 -89.0304337
## 2023.173 -40.2069828 -89.7830646
## 2023.173 -41.1695777 -90.9532783
## 2023.174 -40.9182131 -90.9067457
## 2023.174 -41.4252751 -91.6404351
## 2023.174 -42.3445524 -92.7651581
## 2023.174 -42.1344635 -92.7578708
## 2023.174 -42.6276413 -93.4738344
## 2023.174 -43.5063956 -94.5559149
## 2023.174 -43.3344828 -94.5848195
## 2023.174 -43.8146956 -95.2841766
## 2023.174 -44.6555333 -96.3262730
## 2023.174 -44.5189196 -96.3885366
## 2023.175 -44.9870143 -97.0723194
## 2023.175 -45.7923665 -98.0769127
## 2023.175 -45.6883810 -98.1699078
## 2023.175 -46.1451385 -98.8390682
## 2023.175 -46.9172727 -99.8084745
## 2023.175 -46.8434367 -99.9297641
## 2023.175 -47.2895774 -100.5851809
## 2023.175 -48.0306089 -101.5215623
## 2023.175 -47.9846216 -101.6688864
## 2023.176 -48.4208104 -102.3113716
## 2023.176 -49.1327125 -103.2167468
## 2023.176 -49.1124391 -103.3880099
## 2023.176 -49.5392899 -104.0183140
## 2023.176 -50.2239036 -104.8945680
## 2023.176 -50.2273629 -105.0878273
## 2023.176 -50.6454431 -105.7066446
## 2023.176 -51.3044862 -106.5555378
## 2023.176 -51.3298402 -106.7689925
## 2023.176 -51.7396742 -107.3769656
## 2023.177 -52.3747495 -108.2001421
## 2023.177 -52.4202927 -108.4321231
## 2023.177 -52.8223656 -109.0298474
## 2023.177 -53.4349688 -109.8288429
## 2023.177 -53.4991194 -110.0778035
## 2023.177 -53.8938800 -110.6658307
## 2023.177 -54.4854071 -111.4420800
## 2023.177 -54.5666977 -111.7065871
## 2023.177 -54.9545615 -112.2854290
## 2023.178 -55.5263154 -113.0402726
## 2023.178 -55.6233851 -113.3189986
## 2023.178 -56.0047370 -113.8891302
## 2023.178 -56.5579339 -114.6238207
## 2023.178 -56.6695206 -114.9155360
## 2023.178 -57.0447174 -115.4773985
## 2023.178 -57.5804925 -116.1931065
## 2023.178 -57.7054258 -116.4966724
## 2023.178 -58.0747985 -117.0506757
## 2023.178 -58.5942116 -117.7484954
## 2023.179 -58.7314067 -118.0628577
## 2023.179 -59.0952621 -118.6093832
## 2023.179 -59.5993029 -119.2903375
## 2023.179 -59.7477539 -119.6145201
## 2023.179 -60.1063770 -120.1539228
## 2023.179 -60.5959695 -120.8189680
## 2023.179 -60.7547441 -121.1520678
##
## $upper
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## 80% 95%
## 2023.162 68.66365 75.71091
## 2023.162 62.25352 70.10406
## 2023.162 73.18181 81.74225
## 2023.162 75.95796 86.93842
## 2023.162 69.62725 81.16527
## 2023.162 79.51546 91.57086
## 2023.162 81.15910 94.96005
## 2023.162 75.16404 89.43216
## 2023.162 84.27662 98.98433
## 2023.163 85.39996 101.50866
## 2023.163 79.78366 96.31037
## 2023.163 88.22937 105.15197
## 2023.163 89.05879 107.16305
## 2023.163 83.82218 102.31285
## 2023.163 91.67093 110.52903
## 2023.163 92.31804 112.20252
## 2023.163 87.44850 107.69705
## 2023.163 94.75408 115.34977
## 2023.164 95.28239 116.78738
## 2023.164 90.76223 112.61445
## 2023.164 97.56954 119.75355
## 2023.164 98.01838 121.01966
## 2023.164 93.82813 117.16332
## 2023.164 100.17632 123.83114
## 2023.164 100.57140 124.96899
## 2023.164 96.69125 121.41181
## 2023.164 102.61520 127.64542
## 2023.164 102.97398 128.68531
## 2023.165 99.38446 125.40951
## 2023.165 104.91571 131.24201
## 2023.165 105.25032 132.20582
## 2023.165 101.93265 129.19388
## 2023.165 107.09994 134.65515
## 2023.165 107.41895 135.55904
## 2023.165 104.35523 132.79400
## 2023.165 109.18490 137.91124
## 2023.165 109.49441 138.76740
## 2023.166 106.66770 136.23302
## 2023.166 111.18397 141.03113
## 2023.166 111.48836 141.84884
## 2023.166 108.88264 139.52969
## 2023.166 113.10785 144.03153
## 2023.166 113.41023 144.81797
## 2023.166 111.01049 142.69948
## 2023.166 114.96527 146.92611
## 2023.166 115.26783 147.68685
## 2023.166 113.05996 145.75530
## 2023.167 116.76341 149.72615
## 2023.167 117.06768 150.46557
## 2023.167 115.03847 148.70807
## 2023.167 118.50826 152.44109
## 2023.167 118.81525 153.16264
## 2023.167 116.95236 151.56709
## 2023.167 120.20487 155.07891
## 2023.167 120.51523 155.78531
## 2023.167 118.80710 154.34041
## 2023.168 121.85753 157.64643
## 2023.168 122.17163 158.33985
## 2023.168 120.60749 157.03500
## 2023.168 123.46993 160.14949
## 2023.168 123.78792 160.83165
## 2023.168 122.35771 159.65697
## 2023.168 125.04524 162.59316
## 2023.168 125.36713 163.26544
## 2023.168 124.06146 162.21169
## 2023.168 126.58624 164.98188
## 2023.169 126.91192 165.64537
## 2023.169 125.72205 164.70394
## 2023.169 128.09534 167.31951
## 2023.169 128.42463 167.97509
## 2023.169 127.34240 167.13796
## 2023.169 129.57469 169.60950
## 2023.169 129.90735 170.25787
## 2023.169 128.92514 169.51755
## 2023.169 131.02617 171.85490
## 2023.170 131.36191 172.49661
## 2023.170 130.47266 171.84611
## 2023.170 132.45147 174.05841
## 2023.170 132.78999 174.69391
## 2023.170 131.98710 174.12675
## 2023.170 133.85209 176.22248
## 2023.170 134.19309 176.85213
## 2023.170 133.47040 176.36225
## 2023.170 135.22940 178.34931
## 2023.170 135.57255 178.97339
## 2023.171 134.92435 178.55515
## 2023.171 136.58462 180.44089
## 2023.171 136.92960 181.05962
## 2023.171 136.35057 180.70778
## 2023.171 137.91886 182.49901
## 2023.171 138.26537 183.11258
## 2023.171 137.75054 182.82226
## 2023.171 139.23313 184.52533
## 2023.171 139.58086 185.13388
## 2023.172 139.12562 184.90054
## 2023.172 140.52836 186.52134
## 2023.172 140.87703 187.12499
## 2023.172 140.47708 186.94440
## 2023.172 141.80539 188.48844
## 2023.172 142.15473 189.08729
## 2023.172 141.80607 188.95552
## 2023.172 143.06500 190.42789
## 2023.172 143.41475 191.02201
## 2023.172 143.11367 190.93541
## 2023.173 144.30792 192.34088
## 2023.173 144.65785 192.93033
## 2023.173 144.40087 192.88549
## 2023.173 145.53482 194.22847
## 2023.173 145.88469 194.81333
## 2023.173 145.66860 194.80709
## 2023.173 146.74630 196.09170
## 2023.173 147.09591 196.67199
## 2023.173 146.91772 196.70142
## 2023.174 147.94296 197.93149
## 2023.174 148.29212 198.50728
## 2023.174 148.14903 198.56964
## 2023.174 149.12532 199.74873
## 2023.174 149.47385 200.32004
## 2023.174 149.36328 200.41280
## 2023.174 150.29390 201.54423
## 2023.174 150.64163 202.11111
## 2023.174 150.56117 202.23191
## 2023.174 151.44916 203.31878
## 2023.175 151.79595 203.88126
## 2023.175 151.74335 204.02790
## 2023.175 152.59155 205.07307
## 2023.175 152.93727 205.63120
## 2023.175 152.91044 205.80165
## 2023.175 153.72148 206.80781
## 2023.175 154.06601 207.36161
## 2023.175 154.06302 207.55398
## 2023.175 154.83936 208.52362
## 2023.176 155.18258 209.07314
## 2023.176 155.20163 209.28567
## 2023.176 155.94555 210.22112
## 2023.176 156.28736 210.76639
## 2023.176 156.32678 210.99745
## 2023.176 157.04041 211.90087
## 2023.176 157.38073 212.44193
## 2023.176 157.43896 212.69001
## 2023.176 158.12426 213.56342
## 2023.176 158.46302 214.10031
## 2023.177 158.53860 214.36400
## 2023.177 159.19744 215.20927
## 2023.177 159.53456 215.74204
## 2023.177 159.62616 216.02003
## 2023.177 160.26024 216.83892
## 2023.177 160.59566 217.36761
## 2023.177 160.70203 217.65870
## 2023.177 161.31294 218.45283
## 2023.177 161.64661 218.97748
## 2023.178 161.76659 219.28054
## 2023.178 162.35583 220.05145
## 2023.178 162.68771 220.57210
## 2023.178 162.82021 220.88610
## 2023.178 163.38916 221.63518
## 2023.178 163.71921 222.15189
## 2023.178 163.86323 222.47585
## 2023.178 164.41319 223.20444
## 2023.178 164.74137 223.71725
## 2023.178 164.89599 224.05028
## 2023.179 165.42815 224.75960
## 2023.179 165.75444 225.26856
## 2023.179 165.91879 225.60983
## 2023.179 166.43427 226.30104
## 2023.179 166.75865 226.80620
## 2023.179 166.93193 227.15493
## 2023.179 167.43178 227.82910head(predict_visibility)## $method
## [1] "ARIMA(0,1,5)"
##
## $model
## Series: data_visibility
## ARIMA(0,1,5)
##
## Coefficients:
## ma1 ma2 ma3 ma4 ma5
## -0.4200 -0.1932 -0.0618 -0.159 -0.1324
## s.e. 0.0103 0.0109 0.0117 0.011 0.0102
##
## sigma^2 = 0.6334: log likelihood = -11313.21
## AIC=22638.41 AICc=22638.42 BIC=22681.37
##
## $level
## [1] 80 95
##
## $mean
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## [1] 9.641745 9.497783 9.681415 9.667357 9.528343 9.528343 9.528343 9.528343
## [9] 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343
## [17] 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343
## [25] 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343
## [33] 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343
## [41] 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343
## [49] 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343
## [57] 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343
## [65] 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343
## [73] 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343
## [81] 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343
## [89] 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343
## [97] 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343
## [105] 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343
## [113] 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343
## [121] 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343
## [129] 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343
## [137] 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343
## [145] 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343
## [153] 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343
## [161] 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343 9.528343
##
## $lower
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## 80% 95%
## 2023.162 8.621790 8.081858
## 2023.162 8.318664 7.694477
## 2023.162 8.438026 7.779816
## 2023.162 8.380517 7.699305
## 2023.162 8.230406 7.543320
## 2023.162 8.229954 7.542628
## 2023.162 8.229501 7.541935
## 2023.162 8.229048 7.541243
## 2023.162 8.228596 7.540552
## 2023.163 8.228144 7.539860
## 2023.163 8.227692 7.539169
## 2023.163 8.227240 7.538478
## 2023.163 8.226788 7.537787
## 2023.163 8.226337 7.537096
## 2023.163 8.225885 7.536406
## 2023.163 8.225434 7.535716
## 2023.163 8.224983 7.535026
## 2023.163 8.224532 7.534336
## 2023.164 8.224081 7.533647
## 2023.164 8.223631 7.532958
## 2023.164 8.223180 7.532269
## 2023.164 8.222730 7.531580
## 2023.164 8.222280 7.530892
## 2023.164 8.221830 7.530204
## 2023.164 8.221380 7.529516
## 2023.164 8.220930 7.528828
## 2023.164 8.220481 7.528141
## 2023.164 8.220032 7.527453
## 2023.165 8.219582 7.526766
## 2023.165 8.219133 7.526080
## 2023.165 8.218684 7.525393
## 2023.165 8.218236 7.524707
## 2023.165 8.217787 7.524021
## 2023.165 8.217339 7.523335
## 2023.165 8.216890 7.522649
## 2023.165 8.216442 7.521964
## 2023.165 8.215994 7.521279
## 2023.166 8.215546 7.520594
## 2023.166 8.215099 7.519909
## 2023.166 8.214651 7.519225
## 2023.166 8.214204 7.518541
## 2023.166 8.213757 7.517857
## 2023.166 8.213310 7.517173
## 2023.166 8.212863 7.516489
## 2023.166 8.212416 7.515806
## 2023.166 8.211969 7.515123
## 2023.166 8.211523 7.514440
## 2023.167 8.211076 7.513758
## 2023.167 8.210630 7.513075
## 2023.167 8.210184 7.512393
## 2023.167 8.209738 7.511711
## 2023.167 8.209293 7.511030
## 2023.167 8.208847 7.510348
## 2023.167 8.208402 7.509667
## 2023.167 8.207957 7.508986
## 2023.167 8.207511 7.508305
## 2023.168 8.207066 7.507625
## 2023.168 8.206622 7.506945
## 2023.168 8.206177 7.506265
## 2023.168 8.205733 7.505585
## 2023.168 8.205288 7.504905
## 2023.168 8.204844 7.504226
## 2023.168 8.204400 7.503547
## 2023.168 8.203956 7.502868
## 2023.168 8.203512 7.502189
## 2023.168 8.203069 7.501511
## 2023.169 8.202625 7.500832
## 2023.169 8.202182 7.500155
## 2023.169 8.201739 7.499477
## 2023.169 8.201296 7.498799
## 2023.169 8.200853 7.498122
## 2023.169 8.200410 7.497445
## 2023.169 8.199967 7.496768
## 2023.169 8.199525 7.496091
## 2023.169 8.199083 7.495415
## 2023.170 8.198641 7.494739
## 2023.170 8.198199 7.494063
## 2023.170 8.197757 7.493387
## 2023.170 8.197315 7.492712
## 2023.170 8.196874 7.492036
## 2023.170 8.196432 7.491361
## 2023.170 8.195991 7.490686
## 2023.170 8.195550 7.490012
## 2023.170 8.195109 7.489337
## 2023.170 8.194668 7.488663
## 2023.171 8.194227 7.487989
## 2023.171 8.193787 7.487315
## 2023.171 8.193346 7.486642
## 2023.171 8.192906 7.485969
## 2023.171 8.192466 7.485296
## 2023.171 8.192026 7.484623
## 2023.171 8.191586 7.483950
## 2023.171 8.191147 7.483278
## 2023.171 8.190707 7.482606
## 2023.172 8.190268 7.481934
## 2023.172 8.189829 7.481262
## 2023.172 8.189390 7.480590
## 2023.172 8.188951 7.479919
## 2023.172 8.188512 7.479248
## 2023.172 8.188073 7.478577
## 2023.172 8.187635 7.477907
## 2023.172 8.187196 7.477236
## 2023.172 8.186758 7.476566
## 2023.172 8.186320 7.475896
## 2023.173 8.185882 7.475226
## 2023.173 8.185444 7.474557
## 2023.173 8.185007 7.473887
## 2023.173 8.184569 7.473218
## 2023.173 8.184132 7.472549
## 2023.173 8.183695 7.471881
## 2023.173 8.183258 7.471212
## 2023.173 8.182821 7.470544
## 2023.173 8.182384 7.469876
## 2023.174 8.181947 7.469208
## 2023.174 8.181511 7.468541
## 2023.174 8.181074 7.467873
## 2023.174 8.180638 7.467206
## 2023.174 8.180202 7.466539
## 2023.174 8.179766 7.465872
## 2023.174 8.179330 7.465206
## 2023.174 8.178895 7.464540
## 2023.174 8.178459 7.463874
## 2023.174 8.178024 7.463208
## 2023.175 8.177588 7.462542
## 2023.175 8.177153 7.461877
## 2023.175 8.176718 7.461211
## 2023.175 8.176284 7.460546
## 2023.175 8.175849 7.459882
## 2023.175 8.175414 7.459217
## 2023.175 8.174980 7.458553
## 2023.175 8.174546 7.457889
## 2023.175 8.174112 7.457225
## 2023.176 8.173678 7.456561
## 2023.176 8.173244 7.455897
## 2023.176 8.172810 7.455234
## 2023.176 8.172376 7.454571
## 2023.176 8.171943 7.453908
## 2023.176 8.171510 7.453246
## 2023.176 8.171077 7.452583
## 2023.176 8.170644 7.451921
## 2023.176 8.170211 7.451259
## 2023.176 8.169778 7.450597
## 2023.177 8.169345 7.449935
## 2023.177 8.168913 7.449274
## 2023.177 8.168481 7.448613
## 2023.177 8.168048 7.447952
## 2023.177 8.167616 7.447291
## 2023.177 8.167184 7.446631
## 2023.177 8.166753 7.445970
## 2023.177 8.166321 7.445310
## 2023.177 8.165890 7.444650
## 2023.178 8.165458 7.443990
## 2023.178 8.165027 7.443331
## 2023.178 8.164596 7.442672
## 2023.178 8.164165 7.442013
## 2023.178 8.163734 7.441354
## 2023.178 8.163303 7.440695
## 2023.178 8.162873 7.440037
## 2023.178 8.162442 7.439378
## 2023.178 8.162012 7.438720
## 2023.178 8.161582 7.438062
## 2023.179 8.161152 7.437405
## 2023.179 8.160722 7.436747
## 2023.179 8.160292 7.436090
## 2023.179 8.159863 7.435433
## 2023.179 8.159433 7.434776
## 2023.179 8.159004 7.434120
## 2023.179 8.158575 7.433463
##
## $upper
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## 80% 95%
## 2023.162 10.66170 11.20163
## 2023.162 10.67690 11.30109
## 2023.162 10.92480 11.58301
## 2023.162 10.95420 11.63541
## 2023.162 10.82628 11.51337
## 2023.162 10.82673 11.51406
## 2023.162 10.82719 11.51475
## 2023.162 10.82764 11.51544
## 2023.162 10.82809 11.51613
## 2023.163 10.82854 11.51683
## 2023.163 10.82899 11.51752
## 2023.163 10.82945 11.51821
## 2023.163 10.82990 11.51890
## 2023.163 10.83035 11.51959
## 2023.163 10.83080 11.52028
## 2023.163 10.83125 11.52097
## 2023.163 10.83170 11.52166
## 2023.163 10.83215 11.52235
## 2023.164 10.83260 11.52304
## 2023.164 10.83306 11.52373
## 2023.164 10.83351 11.52442
## 2023.164 10.83396 11.52511
## 2023.164 10.83441 11.52579
## 2023.164 10.83486 11.52648
## 2023.164 10.83531 11.52717
## 2023.164 10.83576 11.52786
## 2023.164 10.83621 11.52855
## 2023.164 10.83665 11.52923
## 2023.165 10.83710 11.52992
## 2023.165 10.83755 11.53061
## 2023.165 10.83800 11.53129
## 2023.165 10.83845 11.53198
## 2023.165 10.83890 11.53267
## 2023.165 10.83935 11.53335
## 2023.165 10.83980 11.53404
## 2023.165 10.84024 11.53472
## 2023.165 10.84069 11.53541
## 2023.166 10.84114 11.53609
## 2023.166 10.84159 11.53678
## 2023.166 10.84203 11.53746
## 2023.166 10.84248 11.53815
## 2023.166 10.84293 11.53883
## 2023.166 10.84338 11.53951
## 2023.166 10.84382 11.54020
## 2023.166 10.84427 11.54088
## 2023.166 10.84472 11.54156
## 2023.166 10.84516 11.54225
## 2023.167 10.84561 11.54293
## 2023.167 10.84606 11.54361
## 2023.167 10.84650 11.54429
## 2023.167 10.84695 11.54497
## 2023.167 10.84739 11.54566
## 2023.167 10.84784 11.54634
## 2023.167 10.84828 11.54702
## 2023.167 10.84873 11.54770
## 2023.167 10.84917 11.54838
## 2023.168 10.84962 11.54906
## 2023.168 10.85006 11.54974
## 2023.168 10.85051 11.55042
## 2023.168 10.85095 11.55110
## 2023.168 10.85140 11.55178
## 2023.168 10.85184 11.55246
## 2023.168 10.85229 11.55314
## 2023.168 10.85273 11.55382
## 2023.168 10.85317 11.55450
## 2023.168 10.85362 11.55518
## 2023.169 10.85406 11.55585
## 2023.169 10.85450 11.55653
## 2023.169 10.85495 11.55721
## 2023.169 10.85539 11.55789
## 2023.169 10.85583 11.55856
## 2023.169 10.85628 11.55924
## 2023.169 10.85672 11.55992
## 2023.169 10.85716 11.56059
## 2023.169 10.85760 11.56127
## 2023.170 10.85805 11.56195
## 2023.170 10.85849 11.56262
## 2023.170 10.85893 11.56330
## 2023.170 10.85937 11.56397
## 2023.170 10.85981 11.56465
## 2023.170 10.86025 11.56532
## 2023.170 10.86070 11.56600
## 2023.170 10.86114 11.56667
## 2023.170 10.86158 11.56735
## 2023.170 10.86202 11.56802
## 2023.171 10.86246 11.56870
## 2023.171 10.86290 11.56937
## 2023.171 10.86334 11.57004
## 2023.171 10.86378 11.57072
## 2023.171 10.86422 11.57139
## 2023.171 10.86466 11.57206
## 2023.171 10.86510 11.57274
## 2023.171 10.86554 11.57341
## 2023.171 10.86598 11.57408
## 2023.172 10.86642 11.57475
## 2023.172 10.86686 11.57542
## 2023.172 10.86730 11.57610
## 2023.172 10.86774 11.57677
## 2023.172 10.86817 11.57744
## 2023.172 10.86861 11.57811
## 2023.172 10.86905 11.57878
## 2023.172 10.86949 11.57945
## 2023.172 10.86993 11.58012
## 2023.172 10.87037 11.58079
## 2023.173 10.87080 11.58146
## 2023.173 10.87124 11.58213
## 2023.173 10.87168 11.58280
## 2023.173 10.87212 11.58347
## 2023.173 10.87255 11.58414
## 2023.173 10.87299 11.58481
## 2023.173 10.87343 11.58547
## 2023.173 10.87387 11.58614
## 2023.173 10.87430 11.58681
## 2023.174 10.87474 11.58748
## 2023.174 10.87518 11.58815
## 2023.174 10.87561 11.58881
## 2023.174 10.87605 11.58948
## 2023.174 10.87648 11.59015
## 2023.174 10.87692 11.59081
## 2023.174 10.87736 11.59148
## 2023.174 10.87779 11.59215
## 2023.174 10.87823 11.59281
## 2023.174 10.87866 11.59348
## 2023.175 10.87910 11.59414
## 2023.175 10.87953 11.59481
## 2023.175 10.87997 11.59547
## 2023.175 10.88040 11.59614
## 2023.175 10.88084 11.59680
## 2023.175 10.88127 11.59747
## 2023.175 10.88171 11.59813
## 2023.175 10.88214 11.59880
## 2023.175 10.88257 11.59946
## 2023.176 10.88301 11.60013
## 2023.176 10.88344 11.60079
## 2023.176 10.88388 11.60145
## 2023.176 10.88431 11.60212
## 2023.176 10.88474 11.60278
## 2023.176 10.88518 11.60344
## 2023.176 10.88561 11.60410
## 2023.176 10.88604 11.60477
## 2023.176 10.88648 11.60543
## 2023.176 10.88691 11.60609
## 2023.177 10.88734 11.60675
## 2023.177 10.88777 11.60741
## 2023.177 10.88821 11.60807
## 2023.177 10.88864 11.60873
## 2023.177 10.88907 11.60940
## 2023.177 10.88950 11.61006
## 2023.177 10.88993 11.61072
## 2023.177 10.89037 11.61138
## 2023.177 10.89080 11.61204
## 2023.178 10.89123 11.61270
## 2023.178 10.89166 11.61336
## 2023.178 10.89209 11.61401
## 2023.178 10.89252 11.61467
## 2023.178 10.89295 11.61533
## 2023.178 10.89338 11.61599
## 2023.178 10.89381 11.61665
## 2023.178 10.89424 11.61731
## 2023.178 10.89467 11.61797
## 2023.178 10.89510 11.61862
## 2023.179 10.89553 11.61928
## 2023.179 10.89596 11.61994
## 2023.179 10.89639 11.62060
## 2023.179 10.89682 11.62125
## 2023.179 10.89725 11.62191
## 2023.179 10.89768 11.62257
## 2023.179 10.89811 11.62322head(predict_solarradiation)## $method
## [1] "ARIMA(5,1,0)"
##
## $model
## Series: data_solarradiation
## ARIMA(5,1,0)
##
## Coefficients:
## ar1 ar2 ar3 ar4 ar5
## 0.5267 -0.0057 0.0230 -0.0400 -0.1406
## s.e. 0.0102 0.0115 0.0115 0.0115 0.0102
##
## sigma^2 = 11029: log likelihood = -57710.08
## AIC=115432.2 AICc=115432.2 BIC=115475.1
##
## $level
## [1] 80 95
##
## $mean
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## [1] 16.92446 39.35447 51.63450 58.36398 61.67737 60.38990 56.20301 52.08563
## [9] 48.83257 46.63199 45.74526 45.96927 46.75070 47.68600 48.52418 49.09400
## [17] 49.34811 49.35071 49.19871 48.98385 48.78133 48.63656 48.56223 48.54922
## [25] 48.57776 48.62543 48.67340 48.71002 48.73082 48.73675 48.73198 48.72170
## [33] 48.71047 48.70134 48.69572 48.69363 48.69425 48.69640 48.69899 48.70123
## [41] 48.70271 48.70337 48.70335 48.70292 48.70233 48.70179 48.70141 48.70121
## [49] 48.70119 48.70127 48.70140 48.70153 48.70162 48.70168 48.70169 48.70168
## [57] 48.70165 48.70162 48.70160 48.70158 48.70158 48.70158 48.70158 48.70159
## [65] 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160
## [73] 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160
## [81] 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160
## [89] 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160
## [97] 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160
## [105] 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160
## [113] 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160
## [121] 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160
## [129] 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160
## [137] 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160
## [145] 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160
## [153] 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160
## [161] 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160 48.70160
##
## $lower
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## 80% 95%
## 2023.162 -117.6608 -188.9060
## 2023.162 -206.2746 -336.3027
## 2023.162 -293.2129 -475.7641
## 2023.162 -375.9355 -605.8398
## 2023.162 -450.5565 -721.7167
## 2023.162 -511.6122 -814.4118
## 2023.162 -562.0478 -889.3301
## 2023.162 -603.5974 -950.6950
## 2023.162 -638.4920 -1002.3397
## 2023.163 -668.9068 -1047.6901
## 2023.163 -696.6960 -1089.7206
## 2023.163 -723.1437 -1130.2875
## 2023.163 -749.1739 -1170.5109
## 2023.163 -775.2093 -1210.8238
## 2023.163 -801.2914 -1251.1566
## 2023.163 -827.2524 -1291.1622
## 2023.163 -852.8622 -1330.4636
## 2023.163 -877.9056 -1368.7655
## 2023.164 -902.2450 -1405.9088
## 2023.164 -925.8326 -1441.8693
## 2023.164 -948.6918 -1476.7221
## 2023.164 -970.8896 -1510.5942
## 2023.164 -992.5139 -1543.6264
## 2023.164 -1013.6507 -1575.9454
## 2023.164 -1034.3707 -1607.6490
## 2023.164 -1054.7248 -1638.8032
## 2023.164 -1074.7447 -1669.4464
## 2023.164 -1094.4475 -1699.5985
## 2023.165 -1113.8412 -1729.2697
## 2023.165 -1132.9305 -1758.4674
## 2023.165 -1151.7201 -1787.2011
## 2023.165 -1170.2168 -1815.4839
## 2023.165 -1188.4303 -1843.3330
## 2023.165 -1206.3720 -1870.7678
## 2023.165 -1224.0549 -1897.8084
## 2023.165 -1241.4917 -1924.4746
## 2023.165 -1258.6945 -1950.7843
## 2023.166 -1275.6741 -1976.7535
## 2023.166 -1292.4400 -2002.3961
## 2023.166 -1309.0005 -2027.7244
## 2023.166 -1325.3629 -2052.7493
## 2023.166 -1341.5337 -2077.4807
## 2023.166 -1357.5190 -2101.9281
## 2023.166 -1373.3246 -2126.1005
## 2023.166 -1388.9563 -2150.0068
## 2023.166 -1404.4195 -2173.6554
## 2023.166 -1419.7195 -2197.0546
## 2023.167 -1434.8615 -2220.2122
## 2023.167 -1449.8504 -2243.1357
## 2023.167 -1464.6908 -2265.8322
## 2023.167 -1479.3871 -2288.3084
## 2023.167 -1493.9435 -2310.5705
## 2023.167 -1508.3639 -2332.6246
## 2023.167 -1522.6520 -2354.4764
## 2023.167 -1536.8114 -2376.1314
## 2023.167 -1550.8455 -2397.5946
## 2023.168 -1564.7575 -2418.8712
## 2023.168 -1578.5506 -2439.9658
## 2023.168 -1592.2277 -2460.8831
## 2023.168 -1605.7917 -2481.6275
## 2023.168 -1619.2454 -2502.2032
## 2023.168 -1632.5915 -2522.6143
## 2023.168 -1645.8324 -2542.8645
## 2023.168 -1658.9707 -2562.9578
## 2023.168 -1672.0087 -2582.8977
## 2023.168 -1684.9486 -2602.6876
## 2023.169 -1697.7927 -2622.3309
## 2023.169 -1710.5430 -2641.8308
## 2023.169 -1723.2015 -2661.1904
## 2023.169 -1735.7703 -2680.4127
## 2023.169 -1748.2511 -2699.5005
## 2023.169 -1760.6459 -2718.4566
## 2023.169 -1772.9563 -2737.2838
## 2023.169 -1785.1841 -2755.9845
## 2023.169 -1797.3309 -2774.5615
## 2023.170 -1809.3983 -2793.0169
## 2023.170 -1821.3878 -2811.3533
## 2023.170 -1833.3009 -2829.5729
## 2023.170 -1845.1391 -2847.6779
## 2023.170 -1856.9038 -2865.6704
## 2023.170 -1868.5963 -2883.5525
## 2023.170 -1880.2179 -2901.3262
## 2023.170 -1891.7699 -2918.9934
## 2023.170 -1903.2535 -2936.5561
## 2023.170 -1914.6700 -2954.0161
## 2023.171 -1926.0204 -2971.3752
## 2023.171 -1937.3060 -2988.6350
## 2023.171 -1948.5279 -3005.7973
## 2023.171 -1959.6870 -3022.8637
## 2023.171 -1970.7845 -3039.8358
## 2023.171 -1981.8213 -3056.7152
## 2023.171 -1992.7984 -3073.5033
## 2023.171 -2003.7169 -3090.2016
## 2023.171 -2014.5775 -3106.8115
## 2023.172 -2025.3813 -3123.3345
## 2023.172 -2036.1291 -3139.7719
## 2023.172 -2046.8218 -3156.1249
## 2023.172 -2057.4602 -3172.3949
## 2023.172 -2068.0451 -3188.5832
## 2023.172 -2078.5774 -3204.6909
## 2023.172 -2089.0578 -3220.7192
## 2023.172 -2099.4870 -3236.6694
## 2023.172 -2109.8659 -3252.5425
## 2023.172 -2120.1951 -3268.3396
## 2023.173 -2130.4753 -3284.0619
## 2023.173 -2140.7073 -3299.7103
## 2023.173 -2150.8916 -3315.2860
## 2023.173 -2161.0291 -3330.7898
## 2023.173 -2171.1202 -3346.2229
## 2023.173 -2181.1657 -3361.5861
## 2023.173 -2191.1661 -3376.8804
## 2023.173 -2201.1220 -3392.1067
## 2023.173 -2211.0341 -3407.2660
## 2023.174 -2220.9029 -3422.3590
## 2023.174 -2230.7290 -3437.3867
## 2023.174 -2240.5129 -3452.3499
## 2023.174 -2250.2552 -3467.2494
## 2023.174 -2259.9564 -3482.0861
## 2023.174 -2269.6169 -3496.8606
## 2023.174 -2279.2374 -3511.5739
## 2023.174 -2288.8183 -3526.2266
## 2023.174 -2298.3601 -3540.8194
## 2023.174 -2307.8632 -3555.3532
## 2023.175 -2317.3282 -3569.8286
## 2023.175 -2326.7554 -3584.2464
## 2023.175 -2336.1454 -3598.6071
## 2023.175 -2345.4986 -3612.9116
## 2023.175 -2354.8153 -3627.1603
## 2023.175 -2364.0961 -3641.3541
## 2023.175 -2373.3413 -3655.4934
## 2023.175 -2382.5514 -3669.5790
## 2023.175 -2391.7267 -3683.6114
## 2023.176 -2400.8677 -3697.5913
## 2023.176 -2409.9746 -3711.5192
## 2023.176 -2419.0480 -3725.3957
## 2023.176 -2428.0881 -3739.2214
## 2023.176 -2437.0953 -3752.9967
## 2023.176 -2446.0701 -3766.7224
## 2023.176 -2455.0126 -3780.3988
## 2023.176 -2463.9233 -3794.0266
## 2023.176 -2472.8026 -3807.6062
## 2023.176 -2481.6506 -3821.1382
## 2023.177 -2490.4679 -3834.6230
## 2023.177 -2499.2546 -3848.0611
## 2023.177 -2508.0111 -3861.4531
## 2023.177 -2516.7378 -3874.7994
## 2023.177 -2525.4348 -3888.1004
## 2023.177 -2534.1026 -3901.3566
## 2023.177 -2542.7414 -3914.5685
## 2023.177 -2551.3515 -3927.7364
## 2023.177 -2559.9331 -3940.8610
## 2023.178 -2568.4867 -3953.9424
## 2023.178 -2577.0123 -3966.9813
## 2023.178 -2585.5104 -3979.9780
## 2023.178 -2593.9811 -3992.9329
## 2023.178 -2602.4248 -4005.8464
## 2023.178 -2610.8417 -4018.7188
## 2023.178 -2619.2320 -4031.5507
## 2023.178 -2627.5960 -4044.3424
## 2023.178 -2635.9340 -4057.0942
## 2023.178 -2644.2461 -4069.8065
## 2023.179 -2652.5326 -4082.4797
## 2023.179 -2660.7939 -4095.1141
## 2023.179 -2669.0300 -4107.7101
## 2023.179 -2677.2412 -4120.2681
## 2023.179 -2685.4277 -4132.7884
## 2023.179 -2693.5899 -4145.2713
## 2023.179 -2701.7278 -4157.7171
##
## $upper
## Time Series:
## Start = 2023.16164383562
## End = 2023.17921538444
## Frequency = 9504
## 80% 95%
## 2023.162 151.5097 222.7549
## 2023.162 284.9835 415.0117
## 2023.162 396.4819 579.0331
## 2023.162 492.6635 722.5678
## 2023.162 573.9112 845.0714
## 2023.162 632.3920 935.1916
## 2023.162 674.4539 1001.7361
## 2023.162 707.7686 1054.8663
## 2023.162 736.1571 1100.0048
## 2023.163 762.1707 1140.9541
## 2023.163 788.1865 1181.2111
## 2023.163 815.0822 1222.2261
## 2023.163 842.6753 1264.0123
## 2023.163 870.5813 1306.1958
## 2023.163 898.3397 1348.2049
## 2023.163 925.4404 1389.3502
## 2023.163 951.5585 1429.1598
## 2023.163 976.6071 1467.4669
## 2023.164 1000.6424 1504.3063
## 2023.164 1023.8003 1539.8370
## 2023.164 1046.2544 1574.2848
## 2023.164 1068.1627 1607.8673
## 2023.164 1089.6384 1640.7508
## 2023.164 1110.7491 1673.0438
## 2023.164 1131.5263 1704.8046
## 2023.164 1151.9757 1736.0540
## 2023.164 1172.0915 1766.7932
## 2023.164 1191.8675 1797.0186
## 2023.165 1211.3029 1826.7314
## 2023.165 1230.4040 1855.9409
## 2023.165 1249.1841 1884.6650
## 2023.165 1267.6602 1912.9273
## 2023.165 1285.8512 1940.7540
## 2023.165 1303.7747 1968.1704
## 2023.165 1321.4463 1995.1998
## 2023.165 1338.8790 2021.8618
## 2023.165 1356.0830 2048.1728
## 2023.166 1373.0669 2074.1463
## 2023.166 1389.8380 2099.7941
## 2023.166 1406.4030 2125.1269
## 2023.166 1422.7683 2150.1547
## 2023.166 1438.9404 2174.8874
## 2023.166 1454.9257 2199.3348
## 2023.166 1470.7305 2223.5064
## 2023.166 1486.3610 2247.4115
## 2023.166 1501.8231 2271.0590
## 2023.166 1517.1223 2294.4574
## 2023.167 1532.2640 2317.6147
## 2023.167 1547.2528 2340.5381
## 2023.167 1562.0934 2363.2347
## 2023.167 1576.7899 2385.7112
## 2023.167 1591.3466 2407.9735
## 2023.167 1605.7672 2430.0279
## 2023.167 1620.0554 2451.8798
## 2023.167 1634.2148 2473.5348
## 2023.167 1648.2489 2494.9980
## 2023.168 1662.1608 2516.2745
## 2023.168 1675.9538 2537.3691
## 2023.168 1689.6309 2558.2863
## 2023.168 1703.1949 2579.0307
## 2023.168 1716.6486 2599.6064
## 2023.168 1729.9946 2620.0174
## 2023.168 1743.2356 2640.2677
## 2023.168 1756.3739 2660.3610
## 2023.168 1769.4119 2680.3009
## 2023.168 1782.3518 2700.0908
## 2023.169 1795.1959 2719.7341
## 2023.169 1807.9462 2739.2340
## 2023.169 1820.6047 2758.5936
## 2023.169 1833.1735 2777.8159
## 2023.169 1845.6543 2796.9037
## 2023.169 1858.0491 2815.8598
## 2023.169 1870.3595 2834.6870
## 2023.169 1882.5873 2853.3877
## 2023.169 1894.7341 2871.9647
## 2023.170 1906.8015 2890.4201
## 2023.170 1918.7910 2908.7565
## 2023.170 1930.7041 2926.9761
## 2023.170 1942.5423 2945.0811
## 2023.170 1954.3070 2963.0736
## 2023.170 1965.9995 2980.9557
## 2023.170 1977.6211 2998.7294
## 2023.170 1989.1731 3016.3966
## 2023.170 2000.6567 3033.9593
## 2023.170 2012.0732 3051.4193
## 2023.171 2023.4236 3068.7783
## 2023.171 2034.7092 3086.0382
## 2023.171 2045.9311 3103.2005
## 2023.171 2057.0902 3120.2669
## 2023.171 2068.1877 3137.2390
## 2023.171 2079.2245 3154.1184
## 2023.171 2090.2016 3170.9065
## 2023.171 2101.1201 3187.6048
## 2023.171 2111.9807 3204.2147
## 2023.172 2122.7845 3220.7377
## 2023.172 2133.5323 3237.1750
## 2023.172 2144.2250 3253.5281
## 2023.172 2154.8634 3269.7981
## 2023.172 2165.4483 3285.9864
## 2023.172 2175.9806 3302.0941
## 2023.172 2186.4610 3318.1224
## 2023.172 2196.8902 3334.0726
## 2023.172 2207.2691 3349.9457
## 2023.172 2217.5983 3365.7428
## 2023.173 2227.8785 3381.4651
## 2023.173 2238.1105 3397.1135
## 2023.173 2248.2948 3412.6891
## 2023.173 2258.4323 3428.1930
## 2023.173 2268.5234 3443.6261
## 2023.173 2278.5689 3458.9893
## 2023.173 2288.5693 3474.2836
## 2023.173 2298.5252 3489.5099
## 2023.173 2308.4373 3504.6692
## 2023.174 2318.3061 3519.7622
## 2023.174 2328.1322 3534.7899
## 2023.174 2337.9161 3549.7531
## 2023.174 2347.6584 3564.6526
## 2023.174 2357.3596 3579.4893
## 2023.174 2367.0201 3594.2638
## 2023.174 2376.6406 3608.9771
## 2023.174 2386.2215 3623.6298
## 2023.174 2395.7632 3638.2226
## 2023.174 2405.2664 3652.7564
## 2023.175 2414.7314 3667.2318
## 2023.175 2424.1586 3681.6496
## 2023.175 2433.5486 3696.0103
## 2023.175 2442.9018 3710.3148
## 2023.175 2452.2185 3724.5635
## 2023.175 2461.4993 3738.7573
## 2023.175 2470.7445 3752.8966
## 2023.175 2479.9546 3766.9822
## 2023.175 2489.1299 3781.0146
## 2023.176 2498.2709 3794.9945
## 2023.176 2507.3778 3808.9224
## 2023.176 2516.4512 3822.7989
## 2023.176 2525.4913 3836.6246
## 2023.176 2534.4985 3850.3999
## 2023.176 2543.4733 3864.1256
## 2023.176 2552.4158 3877.8020
## 2023.176 2561.3265 3891.4298
## 2023.176 2570.2058 3905.0094
## 2023.176 2579.0538 3918.5414
## 2023.177 2587.8711 3932.0262
## 2023.177 2596.6578 3945.4643
## 2023.177 2605.4143 3958.8563
## 2023.177 2614.1410 3972.2026
## 2023.177 2622.8380 3985.5036
## 2023.177 2631.5058 3998.7598
## 2023.177 2640.1446 4011.9717
## 2023.177 2648.7547 4025.1396
## 2023.177 2657.3363 4038.2642
## 2023.178 2665.8899 4051.3456
## 2023.178 2674.4155 4064.3845
## 2023.178 2682.9136 4077.3812
## 2023.178 2691.3843 4090.3361
## 2023.178 2699.8280 4103.2495
## 2023.178 2708.2449 4116.1220
## 2023.178 2716.6352 4128.9539
## 2023.178 2724.9992 4141.7456
## 2023.178 2733.3371 4154.4974
## 2023.178 2741.6493 4167.2097
## 2023.179 2749.9358 4179.8829
## 2023.179 2758.1971 4192.5173
## 2023.179 2766.4332 4205.1133
## 2023.179 2774.6444 4217.6713
## 2023.179 2782.8309 4230.1916
## 2023.179 2790.9931 4242.6745
## 2023.179 2799.1310 4255.12033.3.5 Combine all forecast data
table_forecast <- subset(data.frame(
predict_temp, predict_dew, predict_precip, predict_precipprob,
predict_windgust, predict_windspeed, predict_winddir,
predict_sealevelpressure, predict_cloudcover, predict_visibility,
predict_solarradiation))
table_forecast <- subset(data.frame(
predict_temp, predict_dew, predict_precip, predict_precipprob,
predict_windgust, predict_windspeed, predict_winddir,
predict_sealevelpressure, predict_cloudcover, predict_visibility,
predict_solarradiation),
select = -c(Lo.80, Hi.80, Lo.95, Hi.95, Lo.80.1, Hi.80.1, Lo.95.1, Hi.95.1,
Lo.80.2, Hi.80.2, Lo.95.2, Hi.95.2, Lo.80.3, Hi.80.3, Lo.95.3, Hi.95.3,
Lo.80.4, Hi.80.4, Lo.95.4, Hi.95.4, Lo.80.5, Hi.80.5, Lo.95.5, Hi.95.5,
Lo.80.6, Hi.80.6, Lo.95.6, Hi.95.6, Lo.80.7, Hi.80.7, Lo.95.7, Hi.95.7,
Lo.80.8, Hi.80.8, Lo.95.8, Hi.95.8, Lo.80.9, Hi.80.9, Lo.95.9, Hi.95.9,
Lo.80.10, Hi.80.10, Lo.95.10, Hi.95.10))
colnames(table_forecast) <- c("temp", "dew", "precip", "precipprob",
"windgust", "windspeed", "winddir",
"sealevelpressure", "cloudcover", "visibility",
"solarradiation")3.3.6 DateTime
dtimes = seq.POSIXt(from = as.POSIXct("2023-04-01 00:00:00"), length.out = 168, by = "60 mins")
dtimes## [1] "2023-04-01 00:00:00 +08" "2023-04-01 01:00:00 +08"
## [3] "2023-04-01 02:00:00 +08" "2023-04-01 03:00:00 +08"
## [5] "2023-04-01 04:00:00 +08" "2023-04-01 05:00:00 +08"
## [7] "2023-04-01 06:00:00 +08" "2023-04-01 07:00:00 +08"
## [9] "2023-04-01 08:00:00 +08" "2023-04-01 09:00:00 +08"
## [11] "2023-04-01 10:00:00 +08" "2023-04-01 11:00:00 +08"
## [13] "2023-04-01 12:00:00 +08" "2023-04-01 13:00:00 +08"
## [15] "2023-04-01 14:00:00 +08" "2023-04-01 15:00:00 +08"
## [17] "2023-04-01 16:00:00 +08" "2023-04-01 17:00:00 +08"
## [19] "2023-04-01 18:00:00 +08" "2023-04-01 19:00:00 +08"
## [21] "2023-04-01 20:00:00 +08" "2023-04-01 21:00:00 +08"
## [23] "2023-04-01 22:00:00 +08" "2023-04-01 23:00:00 +08"
## [25] "2023-04-02 00:00:00 +08" "2023-04-02 01:00:00 +08"
## [27] "2023-04-02 02:00:00 +08" "2023-04-02 03:00:00 +08"
## [29] "2023-04-02 04:00:00 +08" "2023-04-02 05:00:00 +08"
## [31] "2023-04-02 06:00:00 +08" "2023-04-02 07:00:00 +08"
## [33] "2023-04-02 08:00:00 +08" "2023-04-02 09:00:00 +08"
## [35] "2023-04-02 10:00:00 +08" "2023-04-02 11:00:00 +08"
## [37] "2023-04-02 12:00:00 +08" "2023-04-02 13:00:00 +08"
## [39] "2023-04-02 14:00:00 +08" "2023-04-02 15:00:00 +08"
## [41] "2023-04-02 16:00:00 +08" "2023-04-02 17:00:00 +08"
## [43] "2023-04-02 18:00:00 +08" "2023-04-02 19:00:00 +08"
## [45] "2023-04-02 20:00:00 +08" "2023-04-02 21:00:00 +08"
## [47] "2023-04-02 22:00:00 +08" "2023-04-02 23:00:00 +08"
## [49] "2023-04-03 00:00:00 +08" "2023-04-03 01:00:00 +08"
## [51] "2023-04-03 02:00:00 +08" "2023-04-03 03:00:00 +08"
## [53] "2023-04-03 04:00:00 +08" "2023-04-03 05:00:00 +08"
## [55] "2023-04-03 06:00:00 +08" "2023-04-03 07:00:00 +08"
## [57] "2023-04-03 08:00:00 +08" "2023-04-03 09:00:00 +08"
## [59] "2023-04-03 10:00:00 +08" "2023-04-03 11:00:00 +08"
## [61] "2023-04-03 12:00:00 +08" "2023-04-03 13:00:00 +08"
## [63] "2023-04-03 14:00:00 +08" "2023-04-03 15:00:00 +08"
## [65] "2023-04-03 16:00:00 +08" "2023-04-03 17:00:00 +08"
## [67] "2023-04-03 18:00:00 +08" "2023-04-03 19:00:00 +08"
## [69] "2023-04-03 20:00:00 +08" "2023-04-03 21:00:00 +08"
## [71] "2023-04-03 22:00:00 +08" "2023-04-03 23:00:00 +08"
## [73] "2023-04-04 00:00:00 +08" "2023-04-04 01:00:00 +08"
## [75] "2023-04-04 02:00:00 +08" "2023-04-04 03:00:00 +08"
## [77] "2023-04-04 04:00:00 +08" "2023-04-04 05:00:00 +08"
## [79] "2023-04-04 06:00:00 +08" "2023-04-04 07:00:00 +08"
## [81] "2023-04-04 08:00:00 +08" "2023-04-04 09:00:00 +08"
## [83] "2023-04-04 10:00:00 +08" "2023-04-04 11:00:00 +08"
## [85] "2023-04-04 12:00:00 +08" "2023-04-04 13:00:00 +08"
## [87] "2023-04-04 14:00:00 +08" "2023-04-04 15:00:00 +08"
## [89] "2023-04-04 16:00:00 +08" "2023-04-04 17:00:00 +08"
## [91] "2023-04-04 18:00:00 +08" "2023-04-04 19:00:00 +08"
## [93] "2023-04-04 20:00:00 +08" "2023-04-04 21:00:00 +08"
## [95] "2023-04-04 22:00:00 +08" "2023-04-04 23:00:00 +08"
## [97] "2023-04-05 00:00:00 +08" "2023-04-05 01:00:00 +08"
## [99] "2023-04-05 02:00:00 +08" "2023-04-05 03:00:00 +08"
## [101] "2023-04-05 04:00:00 +08" "2023-04-05 05:00:00 +08"
## [103] "2023-04-05 06:00:00 +08" "2023-04-05 07:00:00 +08"
## [105] "2023-04-05 08:00:00 +08" "2023-04-05 09:00:00 +08"
## [107] "2023-04-05 10:00:00 +08" "2023-04-05 11:00:00 +08"
## [109] "2023-04-05 12:00:00 +08" "2023-04-05 13:00:00 +08"
## [111] "2023-04-05 14:00:00 +08" "2023-04-05 15:00:00 +08"
## [113] "2023-04-05 16:00:00 +08" "2023-04-05 17:00:00 +08"
## [115] "2023-04-05 18:00:00 +08" "2023-04-05 19:00:00 +08"
## [117] "2023-04-05 20:00:00 +08" "2023-04-05 21:00:00 +08"
## [119] "2023-04-05 22:00:00 +08" "2023-04-05 23:00:00 +08"
## [121] "2023-04-06 00:00:00 +08" "2023-04-06 01:00:00 +08"
## [123] "2023-04-06 02:00:00 +08" "2023-04-06 03:00:00 +08"
## [125] "2023-04-06 04:00:00 +08" "2023-04-06 05:00:00 +08"
## [127] "2023-04-06 06:00:00 +08" "2023-04-06 07:00:00 +08"
## [129] "2023-04-06 08:00:00 +08" "2023-04-06 09:00:00 +08"
## [131] "2023-04-06 10:00:00 +08" "2023-04-06 11:00:00 +08"
## [133] "2023-04-06 12:00:00 +08" "2023-04-06 13:00:00 +08"
## [135] "2023-04-06 14:00:00 +08" "2023-04-06 15:00:00 +08"
## [137] "2023-04-06 16:00:00 +08" "2023-04-06 17:00:00 +08"
## [139] "2023-04-06 18:00:00 +08" "2023-04-06 19:00:00 +08"
## [141] "2023-04-06 20:00:00 +08" "2023-04-06 21:00:00 +08"
## [143] "2023-04-06 22:00:00 +08" "2023-04-06 23:00:00 +08"
## [145] "2023-04-07 00:00:00 +08" "2023-04-07 01:00:00 +08"
## [147] "2023-04-07 02:00:00 +08" "2023-04-07 03:00:00 +08"
## [149] "2023-04-07 04:00:00 +08" "2023-04-07 05:00:00 +08"
## [151] "2023-04-07 06:00:00 +08" "2023-04-07 07:00:00 +08"
## [153] "2023-04-07 08:00:00 +08" "2023-04-07 09:00:00 +08"
## [155] "2023-04-07 10:00:00 +08" "2023-04-07 11:00:00 +08"
## [157] "2023-04-07 12:00:00 +08" "2023-04-07 13:00:00 +08"
## [159] "2023-04-07 14:00:00 +08" "2023-04-07 15:00:00 +08"
## [161] "2023-04-07 16:00:00 +08" "2023-04-07 17:00:00 +08"
## [163] "2023-04-07 18:00:00 +08" "2023-04-07 19:00:00 +08"
## [165] "2023-04-07 20:00:00 +08" "2023-04-07 21:00:00 +08"
## [167] "2023-04-07 22:00:00 +08" "2023-04-07 23:00:00 +08"table_forecast$time_column <- row.names(table_forecast)
row.names(table_forecast) <- dtimes
table_forecast## temp dew precip precipprob windgust windspeed
## 2023-04-01 26.26666 24.57571 0.2726559 4.895384 3.415041 0.4895663
## 2023-04-01 01:00:00 26.65242 24.47688 0.2726559 4.683530 3.932108 0.8623564
## 2023-04-01 02:00:00 27.11316 24.38762 0.2726559 4.469042 4.339261 1.0002948
## 2023-04-01 03:00:00 27.60420 24.31768 0.2726559 4.251985 4.628488 1.0358925
## 2023-04-01 04:00:00 28.08137 24.26165 0.2726559 4.044834 4.840088 1.0358925
## 2023-04-01 05:00:00 28.51383 24.21694 0.2726559 3.846499 4.993563 1.0358925
## 2023-04-01 06:00:00 28.87640 24.18123 0.2726559 3.895667 5.105161 1.0358925
## 2023-04-01 07:00:00 29.15081 24.15271 0.2726559 3.933495 5.186248 1.0358925
## 2023-04-01 08:00:00 29.32629 24.12994 0.2726559 3.959728 5.245178 1.0358925
## 2023-04-01 09:00:00 29.39966 24.11175 0.2726559 3.974140 5.288004 1.0358925
## 2023-04-01 10:00:00 29.37489 24.09723 0.2726559 3.977752 5.319126 1.0358925
## 2023-04-01 11:00:00 29.26217 24.08564 0.2726559 3.971457 5.341743 1.0358925
## 2023-04-01 12:00:00 29.07675 24.07638 0.2726559 3.967764 5.358180 1.0358925
## 2023-04-01 13:00:00 28.83741 24.06898 0.2726559 3.966067 5.370125 1.0358925
## 2023-04-01 14:00:00 28.56492 24.06308 0.2726559 3.965738 5.378805 1.0358925
## 2023-04-01 15:00:00 28.28048 24.05836 0.2726559 3.966136 5.385113 1.0358925
## 2023-04-01 16:00:00 28.00424 24.05460 0.2726559 3.966700 5.389698 1.0358925
## 2023-04-01 17:00:00 27.75406 24.05159 0.2726559 3.966935 5.393029 1.0358925
## 2023-04-01 18:00:00 27.54446 24.04919 0.2726559 3.966978 5.395451 1.0358925
## 2023-04-01 19:00:00 27.38601 24.04727 0.2726559 3.966935 5.397210 1.0358925
## 2023-04-01 20:00:00 27.28489 24.04574 0.2726559 3.966877 5.398489 1.0358925
## 2023-04-01 21:00:00 27.24291 24.04452 0.2726559 3.966841 5.399418 1.0358925
## 2023-04-01 22:00:00 27.25775 24.04354 0.2726559 3.966833 5.400093 1.0358925
## 2023-04-01 23:00:00 27.32345 24.04276 0.2726559 3.966838 5.400584 1.0358925
## 2023-04-02 27.43118 24.04214 0.2726559 3.966845 5.400941 1.0358925
## 2023-04-02 01:00:00 27.57004 24.04164 0.2726559 3.966849 5.401200 1.0358925
## 2023-04-02 02:00:00 27.72798 24.04125 0.2726559 3.966851 5.401388 1.0358925
## 2023-04-02 03:00:00 27.89274 24.04093 0.2726559 3.966850 5.401525 1.0358925
## 2023-04-02 04:00:00 28.05266 24.04068 0.2726559 3.966849 5.401625 1.0358925
## 2023-04-02 05:00:00 28.19740 24.04047 0.2726559 3.966849 5.401697 1.0358925
## 2023-04-02 06:00:00 28.31856 24.04031 0.2726559 3.966849 5.401749 1.0358925
## 2023-04-02 07:00:00 28.41006 24.04018 0.2726559 3.966849 5.401788 1.0358925
## 2023-04-02 08:00:00 28.46832 24.04008 0.2726559 3.966849 5.401815 1.0358925
## 2023-04-02 09:00:00 28.49233 24.04000 0.2726559 3.966849 5.401835 1.0358925
## 2023-04-02 10:00:00 28.48346 24.03993 0.2726559 3.966849 5.401850 1.0358925
## 2023-04-02 11:00:00 28.44516 24.03988 0.2726559 3.966849 5.401861 1.0358925
## 2023-04-02 12:00:00 28.38258 24.03984 0.2726559 3.966849 5.401869 1.0358925
## 2023-04-02 13:00:00 28.30202 24.03981 0.2726559 3.966849 5.401874 1.0358925
## 2023-04-02 14:00:00 28.21046 24.03978 0.2726559 3.966849 5.401878 1.0358925
## 2023-04-02 15:00:00 28.11502 24.03976 0.2726559 3.966849 5.401881 1.0358925
## 2023-04-02 16:00:00 28.02245 24.03974 0.2726559 3.966849 5.401883 1.0358925
## 2023-04-02 17:00:00 27.93872 24.03973 0.2726559 3.966849 5.401885 1.0358925
## 2023-04-02 18:00:00 27.86868 24.03972 0.2726559 3.966849 5.401886 1.0358925
## 2023-04-02 19:00:00 27.81584 24.03971 0.2726559 3.966849 5.401887 1.0358925
## 2023-04-02 20:00:00 27.78227 24.03970 0.2726559 3.966849 5.401887 1.0358925
## 2023-04-02 21:00:00 27.76854 24.03969 0.2726559 3.966849 5.401888 1.0358925
## 2023-04-02 22:00:00 27.77384 24.03969 0.2726559 3.966849 5.401888 1.0358925
## 2023-04-02 23:00:00 27.79616 24.03969 0.2726559 3.966849 5.401888 1.0358925
## 2023-04-03 27.83252 24.03968 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 01:00:00 27.87926 24.03968 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 02:00:00 27.93233 24.03968 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 03:00:00 27.98761 24.03968 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 04:00:00 28.04120 24.03968 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 05:00:00 28.08964 24.03968 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 06:00:00 28.13013 24.03968 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 07:00:00 28.16064 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 08:00:00 28.17998 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 09:00:00 28.18783 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 10:00:00 28.18467 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 11:00:00 28.17166 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 12:00:00 28.15053 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 13:00:00 28.12342 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 14:00:00 28.09266 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 15:00:00 28.06064 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 16:00:00 28.02962 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 17:00:00 28.00159 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 18:00:00 27.97819 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 19:00:00 27.96057 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 20:00:00 27.94943 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 21:00:00 27.94494 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 22:00:00 27.94682 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 23:00:00 27.95441 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 27.96668 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 01:00:00 27.98241 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 02:00:00 28.00024 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 03:00:00 28.01879 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 04:00:00 28.03674 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 05:00:00 28.05296 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 06:00:00 28.06649 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 07:00:00 28.07666 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 08:00:00 28.08308 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 09:00:00 28.08565 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 10:00:00 28.08452 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 11:00:00 28.08010 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 12:00:00 28.07297 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 13:00:00 28.06385 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 14:00:00 28.05351 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 15:00:00 28.04277 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 16:00:00 28.03237 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 17:00:00 28.02299 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 18:00:00 28.01517 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 19:00:00 28.00930 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 20:00:00 28.00560 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 21:00:00 28.00413 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 22:00:00 28.00480 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 23:00:00 28.00738 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 28.01152 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 01:00:00 28.01681 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 02:00:00 28.02280 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 03:00:00 28.02903 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 04:00:00 28.03505 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 05:00:00 28.04047 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 06:00:00 28.04499 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 07:00:00 28.04838 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 08:00:00 28.05051 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 09:00:00 28.05135 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 10:00:00 28.05095 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 11:00:00 28.04945 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 12:00:00 28.04705 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 13:00:00 28.04398 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 14:00:00 28.04050 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 15:00:00 28.03690 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 16:00:00 28.03342 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 17:00:00 28.03028 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 18:00:00 28.02766 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 19:00:00 28.02571 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 20:00:00 28.02448 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 21:00:00 28.02400 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 22:00:00 28.02424 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 23:00:00 28.02511 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 28.02651 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 01:00:00 28.02829 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 02:00:00 28.03030 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 03:00:00 28.03239 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 04:00:00 28.03441 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 05:00:00 28.03622 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 06:00:00 28.03773 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 07:00:00 28.03886 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 08:00:00 28.03957 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 09:00:00 28.03984 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 10:00:00 28.03970 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 11:00:00 28.03919 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 12:00:00 28.03838 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 13:00:00 28.03735 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 14:00:00 28.03618 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 15:00:00 28.03497 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 16:00:00 28.03381 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 17:00:00 28.03276 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 18:00:00 28.03188 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 19:00:00 28.03123 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 20:00:00 28.03082 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 21:00:00 28.03067 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 22:00:00 28.03075 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 23:00:00 28.03105 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 28.03152 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 01:00:00 28.03212 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 02:00:00 28.03279 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 03:00:00 28.03349 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 04:00:00 28.03417 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 05:00:00 28.03478 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 06:00:00 28.03528 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 07:00:00 28.03566 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 08:00:00 28.03589 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 09:00:00 28.03598 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 10:00:00 28.03593 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 11:00:00 28.03576 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 12:00:00 28.03549 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 13:00:00 28.03514 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 14:00:00 28.03475 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 15:00:00 28.03434 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 16:00:00 28.03395 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 17:00:00 28.03360 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 18:00:00 28.03331 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 19:00:00 28.03309 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 20:00:00 28.03295 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 21:00:00 28.03290 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 22:00:00 28.03293 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 23:00:00 28.03303 24.03967 0.2726559 3.966849 5.401889 1.0358925
## winddir sealevelpressure cloudcover visibility
## 2023-04-01 56.47741 1009.738 55.35106 9.641745
## 2023-04-01 01:00:00 77.55992 1009.299 47.42349 9.497783
## 2023-04-01 02:00:00 96.88720 1008.765 57.01075 9.681415
## 2023-04-01 03:00:00 116.74816 1008.155 55.21536 9.667357
## 2023-04-01 04:00:00 129.11703 1007.745 47.83140 9.528343
## 2023-04-01 05:00:00 138.23816 1007.580 56.74228 9.528343
## 2023-04-01 06:00:00 144.54077 1007.665 55.08850 9.528343
## 2023-04-01 07:00:00 147.87510 1007.971 48.21091 9.528343
## 2023-04-01 08:00:00 149.21240 1008.413 56.49309 9.528343
## 2023-04-01 09:00:00 149.03088 1008.861 54.96990 9.528343
## 2023-04-01 10:00:00 147.73147 1009.212 48.56398 9.528343
## 2023-04-01 11:00:00 145.74695 1009.390 56.26180 9.528343
## 2023-04-01 12:00:00 143.38758 1009.364 54.85902 9.528343
## 2023-04-01 13:00:00 140.89601 1009.158 48.89247 9.528343
## 2023-04-01 14:00:00 138.45938 1008.835 56.04713 9.528343
## 2023-04-01 15:00:00 136.20322 1008.484 54.75538 9.528343
## 2023-04-01 16:00:00 134.20643 1008.192 49.19808 9.528343
## 2023-04-01 17:00:00 132.51040 1008.023 55.84788 9.528343
## 2023-04-01 18:00:00 131.12653 1008.010 54.65849 9.528343
## 2023-04-01 19:00:00 130.04474 1008.142 49.48240 9.528343
## 2023-04-01 20:00:00 129.24030 1008.376 55.66295 9.528343
## 2023-04-01 21:00:00 128.67944 1008.647 54.56792 9.528343
## 2023-04-01 22:00:00 128.32402 1008.886 49.74692 9.528343
## 2023-04-01 23:00:00 128.13499 1009.039 55.49130 9.528343
## 2023-04-02 128.07497 1009.076 54.48326 9.528343
## 2023-04-02 01:00:00 128.10990 1008.996 49.99301 9.528343
## 2023-04-02 02:00:00 128.21012 1008.829 55.33200 9.528343
## 2023-04-02 03:00:00 128.35078 1008.623 54.40413 9.528343
## 2023-04-02 04:00:00 128.51189 1008.429 50.22196 9.528343
## 2023-04-02 05:00:00 128.67809 1008.295 55.18414 9.528343
## 2023-04-02 06:00:00 128.83820 1008.247 54.33016 9.528343
## 2023-04-02 07:00:00 128.98464 1008.290 50.43496 9.528343
## 2023-04-02 08:00:00 129.11284 1008.406 55.04691 9.528343
## 2023-04-02 09:00:00 129.22063 1008.562 54.26103 9.528343
## 2023-04-02 10:00:00 129.30766 1008.716 50.63312 9.528343
## 2023-04-02 11:00:00 129.37488 1008.832 54.91955 9.528343
## 2023-04-02 12:00:00 129.42413 1008.884 54.19642 9.528343
## 2023-04-02 13:00:00 129.45777 1008.865 50.81748 9.528343
## 2023-04-02 14:00:00 129.47839 1008.786 54.80134 9.528343
## 2023-04-02 15:00:00 129.48856 1008.670 54.13603 9.528343
## 2023-04-02 16:00:00 129.49073 1008.549 50.98899 9.528343
## 2023-04-02 17:00:00 129.48710 1008.452 54.69163 9.528343
## 2023-04-02 18:00:00 129.47958 1008.400 54.07960 9.528343
## 2023-04-02 19:00:00 129.46974 1008.403 51.14855 9.528343
## 2023-04-02 20:00:00 129.45882 1008.455 54.58981 9.528343
## 2023-04-02 21:00:00 129.44777 1008.540 54.02685 9.528343
## 2023-04-02 22:00:00 129.43729 1008.634 51.29699 9.528343
## 2023-04-02 23:00:00 129.42781 1008.715 54.49532 9.528343
## 2023-04-03 129.41960 1008.763 53.97756 9.528343
## 2023-04-03 01:00:00 129.41277 1008.769 51.43509 9.528343
## 2023-04-03 02:00:00 129.40731 1008.737 54.40762 9.528343
## 2023-04-03 03:00:00 129.40315 1008.676 53.93150 9.528343
## 2023-04-03 04:00:00 129.40015 1008.603 51.56357 9.528343
## 2023-04-03 05:00:00 129.39814 1008.538 54.32623 9.528343
## 2023-04-03 06:00:00 129.39696 1008.494 53.88846 9.528343
## 2023-04-03 07:00:00 129.39644 1008.482 51.68309 9.528343
## 2023-04-03 08:00:00 129.39641 1008.501 54.25069 9.528343
## 2023-04-03 09:00:00 129.39673 1008.545 53.84823 9.528343
## 2023-04-03 10:00:00 129.39728 1008.600 51.79429 9.528343
## 2023-04-03 11:00:00 129.39796 1008.652 54.18059 9.528343
## 2023-04-03 12:00:00 129.39869 1008.690 53.81064 9.528343
## 2023-04-03 13:00:00 129.39942 1008.705 51.89773 9.528343
## 2023-04-03 14:00:00 129.40011 1008.695 54.11553 9.528343
## 2023-04-03 15:00:00 129.40072 1008.665 53.77552 9.528343
## 2023-04-03 16:00:00 129.40125 1008.624 51.99397 9.528343
## 2023-04-03 17:00:00 129.40168 1008.582 54.05515 9.528343
## 2023-04-03 18:00:00 129.40202 1008.550 53.74271 9.528343
## 2023-04-03 19:00:00 129.40228 1008.534 52.08349 9.528343
## 2023-04-03 20:00:00 129.40246 1008.538 53.99912 9.528343
## 2023-04-03 21:00:00 129.40258 1008.558 53.71204 9.528343
## 2023-04-03 22:00:00 129.40264 1008.589 52.16678 9.528343
## 2023-04-03 23:00:00 129.40267 1008.621 53.94712 9.528343
## 2023-04-04 129.40266 1008.648 53.68339 9.528343
## 2023-04-04 01:00:00 129.40264 1008.663 52.24426 9.528343
## 2023-04-04 02:00:00 129.40260 1008.663 53.89886 9.528343
## 2023-04-04 03:00:00 129.40255 1008.650 53.65662 9.528343
## 2023-04-04 04:00:00 129.40250 1008.628 52.31634 9.528343
## 2023-04-04 05:00:00 129.40245 1008.603 53.85408 9.528343
## 2023-04-04 06:00:00 129.40241 1008.581 53.63161 9.528343
## 2023-04-04 07:00:00 129.40237 1008.567 52.38340 9.528343
## 2023-04-04 08:00:00 129.40234 1008.564 53.81252 9.528343
## 2023-04-04 09:00:00 129.40231 1008.572 53.60825 9.528343
## 2023-04-04 10:00:00 129.40229 1008.588 52.44578 9.528343
## 2023-04-04 11:00:00 129.40227 1008.608 53.77396 9.528343
## 2023-04-04 12:00:00 129.40226 1008.626 53.58642 9.528343
## 2023-04-04 13:00:00 129.40225 1008.638 52.50381 9.528343
## 2023-04-04 14:00:00 129.40225 1008.642 53.73817 9.528343
## 2023-04-04 15:00:00 129.40225 1008.637 53.56602 9.528343
## 2023-04-04 16:00:00 129.40225 1008.626 52.55780 9.528343
## 2023-04-04 17:00:00 129.40225 1008.611 53.70496 9.528343
## 2023-04-04 18:00:00 129.40225 1008.597 53.54697 9.528343
## 2023-04-04 19:00:00 129.40226 1008.587 52.60802 9.528343
## 2023-04-04 20:00:00 129.40226 1008.582 53.67414 9.528343
## 2023-04-04 21:00:00 129.40226 1008.584 53.52917 9.528343
## 2023-04-04 22:00:00 129.40227 1008.592 52.65474 9.528343
## 2023-04-04 23:00:00 129.40227 1008.603 53.64554 9.528343
## 2023-04-05 129.40227 1008.614 53.51254 9.528343
## 2023-04-05 01:00:00 129.40227 1008.623 52.69821 9.528343
## 2023-04-05 02:00:00 129.40227 1008.628 53.61900 9.528343
## 2023-04-05 03:00:00 129.40228 1008.627 53.49701 9.528343
## 2023-04-05 04:00:00 129.40228 1008.622 52.73864 9.528343
## 2023-04-05 05:00:00 129.40228 1008.614 53.59437 9.528343
## 2023-04-05 06:00:00 129.40228 1008.605 53.48250 9.528343
## 2023-04-05 07:00:00 129.40228 1008.598 52.77625 9.528343
## 2023-04-05 08:00:00 129.40228 1008.593 53.57152 9.528343
## 2023-04-05 09:00:00 129.40228 1008.593 53.46895 9.528343
## 2023-04-05 10:00:00 129.40228 1008.596 52.81124 9.528343
## 2023-04-05 11:00:00 129.40228 1008.602 53.55031 9.528343
## 2023-04-05 12:00:00 129.40228 1008.609 53.45629 9.528343
## 2023-04-05 13:00:00 129.40228 1008.615 52.84379 9.528343
## 2023-04-05 14:00:00 129.40228 1008.619 53.53063 9.528343
## 2023-04-05 15:00:00 129.40228 1008.620 53.44447 9.528343
## 2023-04-05 16:00:00 129.40228 1008.618 52.87407 9.528343
## 2023-04-05 17:00:00 129.40228 1008.614 53.51237 9.528343
## 2023-04-05 18:00:00 129.40228 1008.608 53.43342 9.528343
## 2023-04-05 19:00:00 129.40228 1008.604 52.90224 9.528343
## 2023-04-05 20:00:00 129.40228 1008.600 53.49543 9.528343
## 2023-04-05 21:00:00 129.40228 1008.599 53.42310 9.528343
## 2023-04-05 22:00:00 129.40228 1008.600 52.92844 9.528343
## 2023-04-05 23:00:00 129.40228 1008.603 53.47971 9.528343
## 2023-04-06 129.40228 1008.607 53.41347 9.528343
## 2023-04-06 01:00:00 129.40228 1008.611 52.95282 9.528343
## 2023-04-06 02:00:00 129.40228 1008.614 53.46512 9.528343
## 2023-04-06 03:00:00 129.40228 1008.615 53.40447 9.528343
## 2023-04-06 04:00:00 129.40228 1008.614 52.97549 9.528343
## 2023-04-06 05:00:00 129.40228 1008.612 53.45158 9.528343
## 2023-04-06 06:00:00 129.40228 1008.610 53.39606 9.528343
## 2023-04-06 07:00:00 129.40228 1008.607 52.99659 9.528343
## 2023-04-06 08:00:00 129.40228 1008.604 53.43902 9.528343
## 2023-04-06 09:00:00 129.40228 1008.603 53.38821 9.528343
## 2023-04-06 10:00:00 129.40228 1008.603 53.01621 9.528343
## 2023-04-06 11:00:00 129.40228 1008.604 53.42737 9.528343
## 2023-04-06 12:00:00 129.40228 1008.606 53.38088 9.528343
## 2023-04-06 13:00:00 129.40228 1008.609 53.03446 9.528343
## 2023-04-06 14:00:00 129.40228 1008.611 53.41655 9.528343
## 2023-04-06 15:00:00 129.40228 1008.612 53.37404 9.528343
## 2023-04-06 16:00:00 129.40228 1008.612 53.05144 9.528343
## 2023-04-06 17:00:00 129.40228 1008.611 53.40652 9.528343
## 2023-04-06 18:00:00 129.40228 1008.610 53.36764 9.528343
## 2023-04-06 19:00:00 129.40228 1008.608 53.06723 9.528343
## 2023-04-06 20:00:00 129.40228 1008.606 53.39721 9.528343
## 2023-04-06 21:00:00 129.40228 1008.605 53.36167 9.528343
## 2023-04-06 22:00:00 129.40228 1008.605 53.08193 9.528343
## 2023-04-06 23:00:00 129.40228 1008.605 53.38857 9.528343
## 2023-04-07 129.40228 1008.606 53.35610 9.528343
## 2023-04-07 01:00:00 129.40228 1008.608 53.09560 9.528343
## 2023-04-07 02:00:00 129.40228 1008.609 53.38056 9.528343
## 2023-04-07 03:00:00 129.40228 1008.610 53.35089 9.528343
## 2023-04-07 04:00:00 129.40228 1008.610 53.10831 9.528343
## 2023-04-07 05:00:00 129.40228 1008.610 53.37312 9.528343
## 2023-04-07 06:00:00 129.40228 1008.609 53.34603 9.528343
## 2023-04-07 07:00:00 129.40228 1008.608 53.12014 9.528343
## 2023-04-07 08:00:00 129.40228 1008.607 53.36622 9.528343
## 2023-04-07 09:00:00 129.40228 1008.607 53.34148 9.528343
## 2023-04-07 10:00:00 129.40228 1008.606 53.13114 9.528343
## 2023-04-07 11:00:00 129.40228 1008.606 53.35982 9.528343
## 2023-04-07 12:00:00 129.40228 1008.607 53.33724 9.528343
## 2023-04-07 13:00:00 129.40228 1008.607 53.14137 9.528343
## 2023-04-07 14:00:00 129.40228 1008.608 53.35388 9.528343
## 2023-04-07 15:00:00 129.40228 1008.609 53.33329 9.528343
## 2023-04-07 16:00:00 129.40228 1008.609 53.15089 9.528343
## 2023-04-07 17:00:00 129.40228 1008.609 53.34837 9.528343
## 2023-04-07 18:00:00 129.40228 1008.609 53.32959 9.528343
## 2023-04-07 19:00:00 129.40228 1008.608 53.15975 9.528343
## 2023-04-07 20:00:00 129.40228 1008.608 53.34326 9.528343
## 2023-04-07 21:00:00 129.40228 1008.607 53.32614 9.528343
## 2023-04-07 22:00:00 129.40228 1008.607 53.16798 9.528343
## 2023-04-07 23:00:00 129.40228 1008.607 53.33852 9.528343
## solarradiation time_column
## 2023-04-01 16.92446 2023.16164
## 2023-04-01 01:00:00 39.35447 2023.16175
## 2023-04-01 02:00:00 51.63450 2023.16185
## 2023-04-01 03:00:00 58.36398 2023.16196
## 2023-04-01 04:00:00 61.67737 2023.16206
## 2023-04-01 05:00:00 60.38990 2023.16217
## 2023-04-01 06:00:00 56.20301 2023.16228
## 2023-04-01 07:00:00 52.08563 2023.16238
## 2023-04-01 08:00:00 48.83257 2023.16249
## 2023-04-01 09:00:00 46.63199 2023.16259
## 2023-04-01 10:00:00 45.74526 2023.16270
## 2023-04-01 11:00:00 45.96927 2023.16280
## 2023-04-01 12:00:00 46.75070 2023.16291
## 2023-04-01 13:00:00 47.68600 2023.16301
## 2023-04-01 14:00:00 48.52418 2023.16312
## 2023-04-01 15:00:00 49.09400 2023.16322
## 2023-04-01 16:00:00 49.34811 2023.16333
## 2023-04-01 17:00:00 49.35071 2023.16343
## 2023-04-01 18:00:00 49.19871 2023.16354
## 2023-04-01 19:00:00 48.98385 2023.16364
## 2023-04-01 20:00:00 48.78133 2023.16375
## 2023-04-01 21:00:00 48.63656 2023.16385
## 2023-04-01 22:00:00 48.56223 2023.16396
## 2023-04-01 23:00:00 48.54922 2023.16406
## 2023-04-02 48.57776 2023.16417
## 2023-04-02 01:00:00 48.62543 2023.16427
## 2023-04-02 02:00:00 48.67340 2023.16438
## 2023-04-02 03:00:00 48.71002 2023.16448
## 2023-04-02 04:00:00 48.73082 2023.16459
## 2023-04-02 05:00:00 48.73675 2023.16470
## 2023-04-02 06:00:00 48.73198 2023.16480
## 2023-04-02 07:00:00 48.72170 2023.16491
## 2023-04-02 08:00:00 48.71047 2023.16501
## 2023-04-02 09:00:00 48.70134 2023.16512
## 2023-04-02 10:00:00 48.69572 2023.16522
## 2023-04-02 11:00:00 48.69363 2023.16533
## 2023-04-02 12:00:00 48.69425 2023.16543
## 2023-04-02 13:00:00 48.69640 2023.16554
## 2023-04-02 14:00:00 48.69899 2023.16564
## 2023-04-02 15:00:00 48.70123 2023.16575
## 2023-04-02 16:00:00 48.70271 2023.16585
## 2023-04-02 17:00:00 48.70337 2023.16596
## 2023-04-02 18:00:00 48.70335 2023.16606
## 2023-04-02 19:00:00 48.70292 2023.16617
## 2023-04-02 20:00:00 48.70233 2023.16627
## 2023-04-02 21:00:00 48.70179 2023.16638
## 2023-04-02 22:00:00 48.70141 2023.16648
## 2023-04-02 23:00:00 48.70121 2023.16659
## 2023-04-03 48.70119 2023.16669
## 2023-04-03 01:00:00 48.70127 2023.16680
## 2023-04-03 02:00:00 48.70140 2023.16690
## 2023-04-03 03:00:00 48.70153 2023.16701
## 2023-04-03 04:00:00 48.70162 2023.16712
## 2023-04-03 05:00:00 48.70168 2023.16722
## 2023-04-03 06:00:00 48.70169 2023.16733
## 2023-04-03 07:00:00 48.70168 2023.16743
## 2023-04-03 08:00:00 48.70165 2023.16754
## 2023-04-03 09:00:00 48.70162 2023.16764
## 2023-04-03 10:00:00 48.70160 2023.16775
## 2023-04-03 11:00:00 48.70158 2023.16785
## 2023-04-03 12:00:00 48.70158 2023.16796
## 2023-04-03 13:00:00 48.70158 2023.16806
## 2023-04-03 14:00:00 48.70158 2023.16817
## 2023-04-03 15:00:00 48.70159 2023.16827
## 2023-04-03 16:00:00 48.70160 2023.16838
## 2023-04-03 17:00:00 48.70160 2023.16848
## 2023-04-03 18:00:00 48.70160 2023.16859
## 2023-04-03 19:00:00 48.70160 2023.16869
## 2023-04-03 20:00:00 48.70160 2023.16880
## 2023-04-03 21:00:00 48.70160 2023.16890
## 2023-04-03 22:00:00 48.70160 2023.16901
## 2023-04-03 23:00:00 48.70160 2023.16911
## 2023-04-04 48.70160 2023.16922
## 2023-04-04 01:00:00 48.70160 2023.16932
## 2023-04-04 02:00:00 48.70160 2023.16943
## 2023-04-04 03:00:00 48.70160 2023.16954
## 2023-04-04 04:00:00 48.70160 2023.16964
## 2023-04-04 05:00:00 48.70160 2023.16975
## 2023-04-04 06:00:00 48.70160 2023.16985
## 2023-04-04 07:00:00 48.70160 2023.16996
## 2023-04-04 08:00:00 48.70160 2023.17006
## 2023-04-04 09:00:00 48.70160 2023.17017
## 2023-04-04 10:00:00 48.70160 2023.17027
## 2023-04-04 11:00:00 48.70160 2023.17038
## 2023-04-04 12:00:00 48.70160 2023.17048
## 2023-04-04 13:00:00 48.70160 2023.17059
## 2023-04-04 14:00:00 48.70160 2023.17069
## 2023-04-04 15:00:00 48.70160 2023.17080
## 2023-04-04 16:00:00 48.70160 2023.17090
## 2023-04-04 17:00:00 48.70160 2023.17101
## 2023-04-04 18:00:00 48.70160 2023.17111
## 2023-04-04 19:00:00 48.70160 2023.17122
## 2023-04-04 20:00:00 48.70160 2023.17132
## 2023-04-04 21:00:00 48.70160 2023.17143
## 2023-04-04 22:00:00 48.70160 2023.17153
## 2023-04-04 23:00:00 48.70160 2023.17164
## 2023-04-05 48.70160 2023.17174
## 2023-04-05 01:00:00 48.70160 2023.17185
## 2023-04-05 02:00:00 48.70160 2023.17196
## 2023-04-05 03:00:00 48.70160 2023.17206
## 2023-04-05 04:00:00 48.70160 2023.17217
## 2023-04-05 05:00:00 48.70160 2023.17227
## 2023-04-05 06:00:00 48.70160 2023.17238
## 2023-04-05 07:00:00 48.70160 2023.17248
## 2023-04-05 08:00:00 48.70160 2023.17259
## 2023-04-05 09:00:00 48.70160 2023.17269
## 2023-04-05 10:00:00 48.70160 2023.17280
## 2023-04-05 11:00:00 48.70160 2023.17290
## 2023-04-05 12:00:00 48.70160 2023.17301
## 2023-04-05 13:00:00 48.70160 2023.17311
## 2023-04-05 14:00:00 48.70160 2023.17322
## 2023-04-05 15:00:00 48.70160 2023.17332
## 2023-04-05 16:00:00 48.70160 2023.17343
## 2023-04-05 17:00:00 48.70160 2023.17353
## 2023-04-05 18:00:00 48.70160 2023.17364
## 2023-04-05 19:00:00 48.70160 2023.17374
## 2023-04-05 20:00:00 48.70160 2023.17385
## 2023-04-05 21:00:00 48.70160 2023.17395
## 2023-04-05 22:00:00 48.70160 2023.17406
## 2023-04-05 23:00:00 48.70160 2023.17416
## 2023-04-06 48.70160 2023.17427
## 2023-04-06 01:00:00 48.70160 2023.17438
## 2023-04-06 02:00:00 48.70160 2023.17448
## 2023-04-06 03:00:00 48.70160 2023.17459
## 2023-04-06 04:00:00 48.70160 2023.17469
## 2023-04-06 05:00:00 48.70160 2023.17480
## 2023-04-06 06:00:00 48.70160 2023.17490
## 2023-04-06 07:00:00 48.70160 2023.17501
## 2023-04-06 08:00:00 48.70160 2023.17511
## 2023-04-06 09:00:00 48.70160 2023.17522
## 2023-04-06 10:00:00 48.70160 2023.17532
## 2023-04-06 11:00:00 48.70160 2023.17543
## 2023-04-06 12:00:00 48.70160 2023.17553
## 2023-04-06 13:00:00 48.70160 2023.17564
## 2023-04-06 14:00:00 48.70160 2023.17574
## 2023-04-06 15:00:00 48.70160 2023.17585
## 2023-04-06 16:00:00 48.70160 2023.17595
## 2023-04-06 17:00:00 48.70160 2023.17606
## 2023-04-06 18:00:00 48.70160 2023.17616
## 2023-04-06 19:00:00 48.70160 2023.17627
## 2023-04-06 20:00:00 48.70160 2023.17637
## 2023-04-06 21:00:00 48.70160 2023.17648
## 2023-04-06 22:00:00 48.70160 2023.17658
## 2023-04-06 23:00:00 48.70160 2023.17669
## 2023-04-07 48.70160 2023.17680
## 2023-04-07 01:00:00 48.70160 2023.17690
## 2023-04-07 02:00:00 48.70160 2023.17701
## 2023-04-07 03:00:00 48.70160 2023.17711
## 2023-04-07 04:00:00 48.70160 2023.17722
## 2023-04-07 05:00:00 48.70160 2023.17732
## 2023-04-07 06:00:00 48.70160 2023.17743
## 2023-04-07 07:00:00 48.70160 2023.17753
## 2023-04-07 08:00:00 48.70160 2023.17764
## 2023-04-07 09:00:00 48.70160 2023.17774
## 2023-04-07 10:00:00 48.70160 2023.17785
## 2023-04-07 11:00:00 48.70160 2023.17795
## 2023-04-07 12:00:00 48.70160 2023.17806
## 2023-04-07 13:00:00 48.70160 2023.17816
## 2023-04-07 14:00:00 48.70160 2023.17827
## 2023-04-07 15:00:00 48.70160 2023.17837
## 2023-04-07 16:00:00 48.70160 2023.17848
## 2023-04-07 17:00:00 48.70160 2023.17858
## 2023-04-07 18:00:00 48.70160 2023.17869
## 2023-04-07 19:00:00 48.70160 2023.17879
## 2023-04-07 20:00:00 48.70160 2023.17890
## 2023-04-07 21:00:00 48.70160 2023.17900
## 2023-04-07 22:00:00 48.70160 2023.17911
## 2023-04-07 23:00:00 48.70160 2023.17922table_forecast = subset(table_forecast, select = -c(time_column))
head(table_forecast)## temp dew precip precipprob windgust windspeed
## 2023-04-01 26.26666 24.57571 0.2726559 4.895384 3.415041 0.4895663
## 2023-04-01 01:00:00 26.65242 24.47688 0.2726559 4.683530 3.932108 0.8623564
## 2023-04-01 02:00:00 27.11316 24.38762 0.2726559 4.469042 4.339261 1.0002948
## 2023-04-01 03:00:00 27.60420 24.31768 0.2726559 4.251985 4.628488 1.0358925
## 2023-04-01 04:00:00 28.08137 24.26165 0.2726559 4.044834 4.840088 1.0358925
## 2023-04-01 05:00:00 28.51383 24.21694 0.2726559 3.846499 4.993563 1.0358925
## winddir sealevelpressure cloudcover visibility
## 2023-04-01 56.47741 1009.738 55.35106 9.641745
## 2023-04-01 01:00:00 77.55992 1009.299 47.42349 9.497783
## 2023-04-01 02:00:00 96.88720 1008.765 57.01075 9.681415
## 2023-04-01 03:00:00 116.74816 1008.155 55.21536 9.667357
## 2023-04-01 04:00:00 129.11703 1007.745 47.83140 9.528343
## 2023-04-01 05:00:00 138.23816 1007.580 56.74228 9.528343
## solarradiation
## 2023-04-01 16.92446
## 2023-04-01 01:00:00 39.35447
## 2023-04-01 02:00:00 51.63450
## 2023-04-01 03:00:00 58.36398
## 2023-04-01 04:00:00 61.67737
## 2023-04-01 05:00:00 60.389903.3.7 Combine Time Series with Classification
print(table_forecast)## temp dew precip precipprob windgust windspeed
## 2023-04-01 26.26666 24.57571 0.2726559 4.895384 3.415041 0.4895663
## 2023-04-01 01:00:00 26.65242 24.47688 0.2726559 4.683530 3.932108 0.8623564
## 2023-04-01 02:00:00 27.11316 24.38762 0.2726559 4.469042 4.339261 1.0002948
## 2023-04-01 03:00:00 27.60420 24.31768 0.2726559 4.251985 4.628488 1.0358925
## 2023-04-01 04:00:00 28.08137 24.26165 0.2726559 4.044834 4.840088 1.0358925
## 2023-04-01 05:00:00 28.51383 24.21694 0.2726559 3.846499 4.993563 1.0358925
## 2023-04-01 06:00:00 28.87640 24.18123 0.2726559 3.895667 5.105161 1.0358925
## 2023-04-01 07:00:00 29.15081 24.15271 0.2726559 3.933495 5.186248 1.0358925
## 2023-04-01 08:00:00 29.32629 24.12994 0.2726559 3.959728 5.245178 1.0358925
## 2023-04-01 09:00:00 29.39966 24.11175 0.2726559 3.974140 5.288004 1.0358925
## 2023-04-01 10:00:00 29.37489 24.09723 0.2726559 3.977752 5.319126 1.0358925
## 2023-04-01 11:00:00 29.26217 24.08564 0.2726559 3.971457 5.341743 1.0358925
## 2023-04-01 12:00:00 29.07675 24.07638 0.2726559 3.967764 5.358180 1.0358925
## 2023-04-01 13:00:00 28.83741 24.06898 0.2726559 3.966067 5.370125 1.0358925
## 2023-04-01 14:00:00 28.56492 24.06308 0.2726559 3.965738 5.378805 1.0358925
## 2023-04-01 15:00:00 28.28048 24.05836 0.2726559 3.966136 5.385113 1.0358925
## 2023-04-01 16:00:00 28.00424 24.05460 0.2726559 3.966700 5.389698 1.0358925
## 2023-04-01 17:00:00 27.75406 24.05159 0.2726559 3.966935 5.393029 1.0358925
## 2023-04-01 18:00:00 27.54446 24.04919 0.2726559 3.966978 5.395451 1.0358925
## 2023-04-01 19:00:00 27.38601 24.04727 0.2726559 3.966935 5.397210 1.0358925
## 2023-04-01 20:00:00 27.28489 24.04574 0.2726559 3.966877 5.398489 1.0358925
## 2023-04-01 21:00:00 27.24291 24.04452 0.2726559 3.966841 5.399418 1.0358925
## 2023-04-01 22:00:00 27.25775 24.04354 0.2726559 3.966833 5.400093 1.0358925
## 2023-04-01 23:00:00 27.32345 24.04276 0.2726559 3.966838 5.400584 1.0358925
## 2023-04-02 27.43118 24.04214 0.2726559 3.966845 5.400941 1.0358925
## 2023-04-02 01:00:00 27.57004 24.04164 0.2726559 3.966849 5.401200 1.0358925
## 2023-04-02 02:00:00 27.72798 24.04125 0.2726559 3.966851 5.401388 1.0358925
## 2023-04-02 03:00:00 27.89274 24.04093 0.2726559 3.966850 5.401525 1.0358925
## 2023-04-02 04:00:00 28.05266 24.04068 0.2726559 3.966849 5.401625 1.0358925
## 2023-04-02 05:00:00 28.19740 24.04047 0.2726559 3.966849 5.401697 1.0358925
## 2023-04-02 06:00:00 28.31856 24.04031 0.2726559 3.966849 5.401749 1.0358925
## 2023-04-02 07:00:00 28.41006 24.04018 0.2726559 3.966849 5.401788 1.0358925
## 2023-04-02 08:00:00 28.46832 24.04008 0.2726559 3.966849 5.401815 1.0358925
## 2023-04-02 09:00:00 28.49233 24.04000 0.2726559 3.966849 5.401835 1.0358925
## 2023-04-02 10:00:00 28.48346 24.03993 0.2726559 3.966849 5.401850 1.0358925
## 2023-04-02 11:00:00 28.44516 24.03988 0.2726559 3.966849 5.401861 1.0358925
## 2023-04-02 12:00:00 28.38258 24.03984 0.2726559 3.966849 5.401869 1.0358925
## 2023-04-02 13:00:00 28.30202 24.03981 0.2726559 3.966849 5.401874 1.0358925
## 2023-04-02 14:00:00 28.21046 24.03978 0.2726559 3.966849 5.401878 1.0358925
## 2023-04-02 15:00:00 28.11502 24.03976 0.2726559 3.966849 5.401881 1.0358925
## 2023-04-02 16:00:00 28.02245 24.03974 0.2726559 3.966849 5.401883 1.0358925
## 2023-04-02 17:00:00 27.93872 24.03973 0.2726559 3.966849 5.401885 1.0358925
## 2023-04-02 18:00:00 27.86868 24.03972 0.2726559 3.966849 5.401886 1.0358925
## 2023-04-02 19:00:00 27.81584 24.03971 0.2726559 3.966849 5.401887 1.0358925
## 2023-04-02 20:00:00 27.78227 24.03970 0.2726559 3.966849 5.401887 1.0358925
## 2023-04-02 21:00:00 27.76854 24.03969 0.2726559 3.966849 5.401888 1.0358925
## 2023-04-02 22:00:00 27.77384 24.03969 0.2726559 3.966849 5.401888 1.0358925
## 2023-04-02 23:00:00 27.79616 24.03969 0.2726559 3.966849 5.401888 1.0358925
## 2023-04-03 27.83252 24.03968 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 01:00:00 27.87926 24.03968 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 02:00:00 27.93233 24.03968 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 03:00:00 27.98761 24.03968 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 04:00:00 28.04120 24.03968 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 05:00:00 28.08964 24.03968 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 06:00:00 28.13013 24.03968 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 07:00:00 28.16064 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 08:00:00 28.17998 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 09:00:00 28.18783 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 10:00:00 28.18467 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 11:00:00 28.17166 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 12:00:00 28.15053 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 13:00:00 28.12342 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 14:00:00 28.09266 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 15:00:00 28.06064 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 16:00:00 28.02962 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 17:00:00 28.00159 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 18:00:00 27.97819 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 19:00:00 27.96057 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 20:00:00 27.94943 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 21:00:00 27.94494 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 22:00:00 27.94682 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-03 23:00:00 27.95441 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 27.96668 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 01:00:00 27.98241 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 02:00:00 28.00024 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 03:00:00 28.01879 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 04:00:00 28.03674 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 05:00:00 28.05296 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 06:00:00 28.06649 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 07:00:00 28.07666 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 08:00:00 28.08308 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 09:00:00 28.08565 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 10:00:00 28.08452 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 11:00:00 28.08010 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 12:00:00 28.07297 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 13:00:00 28.06385 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 14:00:00 28.05351 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 15:00:00 28.04277 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 16:00:00 28.03237 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 17:00:00 28.02299 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 18:00:00 28.01517 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 19:00:00 28.00930 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 20:00:00 28.00560 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 21:00:00 28.00413 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 22:00:00 28.00480 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-04 23:00:00 28.00738 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 28.01152 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 01:00:00 28.01681 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 02:00:00 28.02280 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 03:00:00 28.02903 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 04:00:00 28.03505 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 05:00:00 28.04047 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 06:00:00 28.04499 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 07:00:00 28.04838 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 08:00:00 28.05051 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 09:00:00 28.05135 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 10:00:00 28.05095 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 11:00:00 28.04945 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 12:00:00 28.04705 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 13:00:00 28.04398 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 14:00:00 28.04050 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 15:00:00 28.03690 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 16:00:00 28.03342 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 17:00:00 28.03028 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 18:00:00 28.02766 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 19:00:00 28.02571 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 20:00:00 28.02448 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 21:00:00 28.02400 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 22:00:00 28.02424 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-05 23:00:00 28.02511 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 28.02651 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 01:00:00 28.02829 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 02:00:00 28.03030 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 03:00:00 28.03239 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 04:00:00 28.03441 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 05:00:00 28.03622 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 06:00:00 28.03773 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 07:00:00 28.03886 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 08:00:00 28.03957 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 09:00:00 28.03984 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 10:00:00 28.03970 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 11:00:00 28.03919 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 12:00:00 28.03838 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 13:00:00 28.03735 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 14:00:00 28.03618 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 15:00:00 28.03497 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 16:00:00 28.03381 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 17:00:00 28.03276 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 18:00:00 28.03188 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 19:00:00 28.03123 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 20:00:00 28.03082 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 21:00:00 28.03067 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 22:00:00 28.03075 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-06 23:00:00 28.03105 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 28.03152 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 01:00:00 28.03212 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 02:00:00 28.03279 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 03:00:00 28.03349 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 04:00:00 28.03417 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 05:00:00 28.03478 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 06:00:00 28.03528 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 07:00:00 28.03566 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 08:00:00 28.03589 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 09:00:00 28.03598 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 10:00:00 28.03593 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 11:00:00 28.03576 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 12:00:00 28.03549 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 13:00:00 28.03514 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 14:00:00 28.03475 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 15:00:00 28.03434 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 16:00:00 28.03395 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 17:00:00 28.03360 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 18:00:00 28.03331 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 19:00:00 28.03309 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 20:00:00 28.03295 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 21:00:00 28.03290 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 22:00:00 28.03293 24.03967 0.2726559 3.966849 5.401889 1.0358925
## 2023-04-07 23:00:00 28.03303 24.03967 0.2726559 3.966849 5.401889 1.0358925
## winddir sealevelpressure cloudcover visibility
## 2023-04-01 56.47741 1009.738 55.35106 9.641745
## 2023-04-01 01:00:00 77.55992 1009.299 47.42349 9.497783
## 2023-04-01 02:00:00 96.88720 1008.765 57.01075 9.681415
## 2023-04-01 03:00:00 116.74816 1008.155 55.21536 9.667357
## 2023-04-01 04:00:00 129.11703 1007.745 47.83140 9.528343
## 2023-04-01 05:00:00 138.23816 1007.580 56.74228 9.528343
## 2023-04-01 06:00:00 144.54077 1007.665 55.08850 9.528343
## 2023-04-01 07:00:00 147.87510 1007.971 48.21091 9.528343
## 2023-04-01 08:00:00 149.21240 1008.413 56.49309 9.528343
## 2023-04-01 09:00:00 149.03088 1008.861 54.96990 9.528343
## 2023-04-01 10:00:00 147.73147 1009.212 48.56398 9.528343
## 2023-04-01 11:00:00 145.74695 1009.390 56.26180 9.528343
## 2023-04-01 12:00:00 143.38758 1009.364 54.85902 9.528343
## 2023-04-01 13:00:00 140.89601 1009.158 48.89247 9.528343
## 2023-04-01 14:00:00 138.45938 1008.835 56.04713 9.528343
## 2023-04-01 15:00:00 136.20322 1008.484 54.75538 9.528343
## 2023-04-01 16:00:00 134.20643 1008.192 49.19808 9.528343
## 2023-04-01 17:00:00 132.51040 1008.023 55.84788 9.528343
## 2023-04-01 18:00:00 131.12653 1008.010 54.65849 9.528343
## 2023-04-01 19:00:00 130.04474 1008.142 49.48240 9.528343
## 2023-04-01 20:00:00 129.24030 1008.376 55.66295 9.528343
## 2023-04-01 21:00:00 128.67944 1008.647 54.56792 9.528343
## 2023-04-01 22:00:00 128.32402 1008.886 49.74692 9.528343
## 2023-04-01 23:00:00 128.13499 1009.039 55.49130 9.528343
## 2023-04-02 128.07497 1009.076 54.48326 9.528343
## 2023-04-02 01:00:00 128.10990 1008.996 49.99301 9.528343
## 2023-04-02 02:00:00 128.21012 1008.829 55.33200 9.528343
## 2023-04-02 03:00:00 128.35078 1008.623 54.40413 9.528343
## 2023-04-02 04:00:00 128.51189 1008.429 50.22196 9.528343
## 2023-04-02 05:00:00 128.67809 1008.295 55.18414 9.528343
## 2023-04-02 06:00:00 128.83820 1008.247 54.33016 9.528343
## 2023-04-02 07:00:00 128.98464 1008.290 50.43496 9.528343
## 2023-04-02 08:00:00 129.11284 1008.406 55.04691 9.528343
## 2023-04-02 09:00:00 129.22063 1008.562 54.26103 9.528343
## 2023-04-02 10:00:00 129.30766 1008.716 50.63312 9.528343
## 2023-04-02 11:00:00 129.37488 1008.832 54.91955 9.528343
## 2023-04-02 12:00:00 129.42413 1008.884 54.19642 9.528343
## 2023-04-02 13:00:00 129.45777 1008.865 50.81748 9.528343
## 2023-04-02 14:00:00 129.47839 1008.786 54.80134 9.528343
## 2023-04-02 15:00:00 129.48856 1008.670 54.13603 9.528343
## 2023-04-02 16:00:00 129.49073 1008.549 50.98899 9.528343
## 2023-04-02 17:00:00 129.48710 1008.452 54.69163 9.528343
## 2023-04-02 18:00:00 129.47958 1008.400 54.07960 9.528343
## 2023-04-02 19:00:00 129.46974 1008.403 51.14855 9.528343
## 2023-04-02 20:00:00 129.45882 1008.455 54.58981 9.528343
## 2023-04-02 21:00:00 129.44777 1008.540 54.02685 9.528343
## 2023-04-02 22:00:00 129.43729 1008.634 51.29699 9.528343
## 2023-04-02 23:00:00 129.42781 1008.715 54.49532 9.528343
## 2023-04-03 129.41960 1008.763 53.97756 9.528343
## 2023-04-03 01:00:00 129.41277 1008.769 51.43509 9.528343
## 2023-04-03 02:00:00 129.40731 1008.737 54.40762 9.528343
## 2023-04-03 03:00:00 129.40315 1008.676 53.93150 9.528343
## 2023-04-03 04:00:00 129.40015 1008.603 51.56357 9.528343
## 2023-04-03 05:00:00 129.39814 1008.538 54.32623 9.528343
## 2023-04-03 06:00:00 129.39696 1008.494 53.88846 9.528343
## 2023-04-03 07:00:00 129.39644 1008.482 51.68309 9.528343
## 2023-04-03 08:00:00 129.39641 1008.501 54.25069 9.528343
## 2023-04-03 09:00:00 129.39673 1008.545 53.84823 9.528343
## 2023-04-03 10:00:00 129.39728 1008.600 51.79429 9.528343
## 2023-04-03 11:00:00 129.39796 1008.652 54.18059 9.528343
## 2023-04-03 12:00:00 129.39869 1008.690 53.81064 9.528343
## 2023-04-03 13:00:00 129.39942 1008.705 51.89773 9.528343
## 2023-04-03 14:00:00 129.40011 1008.695 54.11553 9.528343
## 2023-04-03 15:00:00 129.40072 1008.665 53.77552 9.528343
## 2023-04-03 16:00:00 129.40125 1008.624 51.99397 9.528343
## 2023-04-03 17:00:00 129.40168 1008.582 54.05515 9.528343
## 2023-04-03 18:00:00 129.40202 1008.550 53.74271 9.528343
## 2023-04-03 19:00:00 129.40228 1008.534 52.08349 9.528343
## 2023-04-03 20:00:00 129.40246 1008.538 53.99912 9.528343
## 2023-04-03 21:00:00 129.40258 1008.558 53.71204 9.528343
## 2023-04-03 22:00:00 129.40264 1008.589 52.16678 9.528343
## 2023-04-03 23:00:00 129.40267 1008.621 53.94712 9.528343
## 2023-04-04 129.40266 1008.648 53.68339 9.528343
## 2023-04-04 01:00:00 129.40264 1008.663 52.24426 9.528343
## 2023-04-04 02:00:00 129.40260 1008.663 53.89886 9.528343
## 2023-04-04 03:00:00 129.40255 1008.650 53.65662 9.528343
## 2023-04-04 04:00:00 129.40250 1008.628 52.31634 9.528343
## 2023-04-04 05:00:00 129.40245 1008.603 53.85408 9.528343
## 2023-04-04 06:00:00 129.40241 1008.581 53.63161 9.528343
## 2023-04-04 07:00:00 129.40237 1008.567 52.38340 9.528343
## 2023-04-04 08:00:00 129.40234 1008.564 53.81252 9.528343
## 2023-04-04 09:00:00 129.40231 1008.572 53.60825 9.528343
## 2023-04-04 10:00:00 129.40229 1008.588 52.44578 9.528343
## 2023-04-04 11:00:00 129.40227 1008.608 53.77396 9.528343
## 2023-04-04 12:00:00 129.40226 1008.626 53.58642 9.528343
## 2023-04-04 13:00:00 129.40225 1008.638 52.50381 9.528343
## 2023-04-04 14:00:00 129.40225 1008.642 53.73817 9.528343
## 2023-04-04 15:00:00 129.40225 1008.637 53.56602 9.528343
## 2023-04-04 16:00:00 129.40225 1008.626 52.55780 9.528343
## 2023-04-04 17:00:00 129.40225 1008.611 53.70496 9.528343
## 2023-04-04 18:00:00 129.40225 1008.597 53.54697 9.528343
## 2023-04-04 19:00:00 129.40226 1008.587 52.60802 9.528343
## 2023-04-04 20:00:00 129.40226 1008.582 53.67414 9.528343
## 2023-04-04 21:00:00 129.40226 1008.584 53.52917 9.528343
## 2023-04-04 22:00:00 129.40227 1008.592 52.65474 9.528343
## 2023-04-04 23:00:00 129.40227 1008.603 53.64554 9.528343
## 2023-04-05 129.40227 1008.614 53.51254 9.528343
## 2023-04-05 01:00:00 129.40227 1008.623 52.69821 9.528343
## 2023-04-05 02:00:00 129.40227 1008.628 53.61900 9.528343
## 2023-04-05 03:00:00 129.40228 1008.627 53.49701 9.528343
## 2023-04-05 04:00:00 129.40228 1008.622 52.73864 9.528343
## 2023-04-05 05:00:00 129.40228 1008.614 53.59437 9.528343
## 2023-04-05 06:00:00 129.40228 1008.605 53.48250 9.528343
## 2023-04-05 07:00:00 129.40228 1008.598 52.77625 9.528343
## 2023-04-05 08:00:00 129.40228 1008.593 53.57152 9.528343
## 2023-04-05 09:00:00 129.40228 1008.593 53.46895 9.528343
## 2023-04-05 10:00:00 129.40228 1008.596 52.81124 9.528343
## 2023-04-05 11:00:00 129.40228 1008.602 53.55031 9.528343
## 2023-04-05 12:00:00 129.40228 1008.609 53.45629 9.528343
## 2023-04-05 13:00:00 129.40228 1008.615 52.84379 9.528343
## 2023-04-05 14:00:00 129.40228 1008.619 53.53063 9.528343
## 2023-04-05 15:00:00 129.40228 1008.620 53.44447 9.528343
## 2023-04-05 16:00:00 129.40228 1008.618 52.87407 9.528343
## 2023-04-05 17:00:00 129.40228 1008.614 53.51237 9.528343
## 2023-04-05 18:00:00 129.40228 1008.608 53.43342 9.528343
## 2023-04-05 19:00:00 129.40228 1008.604 52.90224 9.528343
## 2023-04-05 20:00:00 129.40228 1008.600 53.49543 9.528343
## 2023-04-05 21:00:00 129.40228 1008.599 53.42310 9.528343
## 2023-04-05 22:00:00 129.40228 1008.600 52.92844 9.528343
## 2023-04-05 23:00:00 129.40228 1008.603 53.47971 9.528343
## 2023-04-06 129.40228 1008.607 53.41347 9.528343
## 2023-04-06 01:00:00 129.40228 1008.611 52.95282 9.528343
## 2023-04-06 02:00:00 129.40228 1008.614 53.46512 9.528343
## 2023-04-06 03:00:00 129.40228 1008.615 53.40447 9.528343
## 2023-04-06 04:00:00 129.40228 1008.614 52.97549 9.528343
## 2023-04-06 05:00:00 129.40228 1008.612 53.45158 9.528343
## 2023-04-06 06:00:00 129.40228 1008.610 53.39606 9.528343
## 2023-04-06 07:00:00 129.40228 1008.607 52.99659 9.528343
## 2023-04-06 08:00:00 129.40228 1008.604 53.43902 9.528343
## 2023-04-06 09:00:00 129.40228 1008.603 53.38821 9.528343
## 2023-04-06 10:00:00 129.40228 1008.603 53.01621 9.528343
## 2023-04-06 11:00:00 129.40228 1008.604 53.42737 9.528343
## 2023-04-06 12:00:00 129.40228 1008.606 53.38088 9.528343
## 2023-04-06 13:00:00 129.40228 1008.609 53.03446 9.528343
## 2023-04-06 14:00:00 129.40228 1008.611 53.41655 9.528343
## 2023-04-06 15:00:00 129.40228 1008.612 53.37404 9.528343
## 2023-04-06 16:00:00 129.40228 1008.612 53.05144 9.528343
## 2023-04-06 17:00:00 129.40228 1008.611 53.40652 9.528343
## 2023-04-06 18:00:00 129.40228 1008.610 53.36764 9.528343
## 2023-04-06 19:00:00 129.40228 1008.608 53.06723 9.528343
## 2023-04-06 20:00:00 129.40228 1008.606 53.39721 9.528343
## 2023-04-06 21:00:00 129.40228 1008.605 53.36167 9.528343
## 2023-04-06 22:00:00 129.40228 1008.605 53.08193 9.528343
## 2023-04-06 23:00:00 129.40228 1008.605 53.38857 9.528343
## 2023-04-07 129.40228 1008.606 53.35610 9.528343
## 2023-04-07 01:00:00 129.40228 1008.608 53.09560 9.528343
## 2023-04-07 02:00:00 129.40228 1008.609 53.38056 9.528343
## 2023-04-07 03:00:00 129.40228 1008.610 53.35089 9.528343
## 2023-04-07 04:00:00 129.40228 1008.610 53.10831 9.528343
## 2023-04-07 05:00:00 129.40228 1008.610 53.37312 9.528343
## 2023-04-07 06:00:00 129.40228 1008.609 53.34603 9.528343
## 2023-04-07 07:00:00 129.40228 1008.608 53.12014 9.528343
## 2023-04-07 08:00:00 129.40228 1008.607 53.36622 9.528343
## 2023-04-07 09:00:00 129.40228 1008.607 53.34148 9.528343
## 2023-04-07 10:00:00 129.40228 1008.606 53.13114 9.528343
## 2023-04-07 11:00:00 129.40228 1008.606 53.35982 9.528343
## 2023-04-07 12:00:00 129.40228 1008.607 53.33724 9.528343
## 2023-04-07 13:00:00 129.40228 1008.607 53.14137 9.528343
## 2023-04-07 14:00:00 129.40228 1008.608 53.35388 9.528343
## 2023-04-07 15:00:00 129.40228 1008.609 53.33329 9.528343
## 2023-04-07 16:00:00 129.40228 1008.609 53.15089 9.528343
## 2023-04-07 17:00:00 129.40228 1008.609 53.34837 9.528343
## 2023-04-07 18:00:00 129.40228 1008.609 53.32959 9.528343
## 2023-04-07 19:00:00 129.40228 1008.608 53.15975 9.528343
## 2023-04-07 20:00:00 129.40228 1008.608 53.34326 9.528343
## 2023-04-07 21:00:00 129.40228 1008.607 53.32614 9.528343
## 2023-04-07 22:00:00 129.40228 1008.607 53.16798 9.528343
## 2023-04-07 23:00:00 129.40228 1008.607 53.33852 9.528343
## solarradiation
## 2023-04-01 16.92446
## 2023-04-01 01:00:00 39.35447
## 2023-04-01 02:00:00 51.63450
## 2023-04-01 03:00:00 58.36398
## 2023-04-01 04:00:00 61.67737
## 2023-04-01 05:00:00 60.38990
## 2023-04-01 06:00:00 56.20301
## 2023-04-01 07:00:00 52.08563
## 2023-04-01 08:00:00 48.83257
## 2023-04-01 09:00:00 46.63199
## 2023-04-01 10:00:00 45.74526
## 2023-04-01 11:00:00 45.96927
## 2023-04-01 12:00:00 46.75070
## 2023-04-01 13:00:00 47.68600
## 2023-04-01 14:00:00 48.52418
## 2023-04-01 15:00:00 49.09400
## 2023-04-01 16:00:00 49.34811
## 2023-04-01 17:00:00 49.35071
## 2023-04-01 18:00:00 49.19871
## 2023-04-01 19:00:00 48.98385
## 2023-04-01 20:00:00 48.78133
## 2023-04-01 21:00:00 48.63656
## 2023-04-01 22:00:00 48.56223
## 2023-04-01 23:00:00 48.54922
## 2023-04-02 48.57776
## 2023-04-02 01:00:00 48.62543
## 2023-04-02 02:00:00 48.67340
## 2023-04-02 03:00:00 48.71002
## 2023-04-02 04:00:00 48.73082
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## 2023-04-07 23:00:00 48.70160final_preds = predict(final_classifier_RF, newdata = table_forecast)
head(final_preds)## 2023-04-01 2023-04-01 01:00:00 2023-04-01 02:00:00 2023-04-01 03:00:00
## no rain no rain no rain rain
## 2023-04-01 04:00:00 2023-04-01 05:00:00
## rain rain
## Levels: no rain rainoutcome_df = data.frame(final_preds)
outcome_df$time_column <- row.names(outcome_df)
row.names(outcome_df) <- dtimes
head(outcome_df)## final_preds time_column
## 2023-04-01 no rain 2023-04-01
## 2023-04-01 01:00:00 no rain 2023-04-01 01:00:00
## 2023-04-01 02:00:00 no rain 2023-04-01 02:00:00
## 2023-04-01 03:00:00 rain 2023-04-01 03:00:00
## 2023-04-01 04:00:00 rain 2023-04-01 04:00:00
## 2023-04-01 05:00:00 rain 2023-04-01 05:00:00outcome_df$time_column <- row.names(outcome_df)
row.names(outcome_df) <- NULL
head(outcome_df)## final_preds time_column
## 1 no rain 2023-04-01
## 2 no rain 2023-04-01 01:00:00
## 3 no rain 2023-04-01 02:00:00
## 4 rain 2023-04-01 03:00:00
## 5 rain 2023-04-01 04:00:00
## 6 rain 2023-04-01 05:00:004 Evaluation
4.1 To find which p value (training set) are suitable. p = 0.6 - Cross-Validation
p_values <- c(0.6, 0.7, 0.8)
results <- numeric(length(p_values))
for (i in seq_along(p_values)) {
train_idx <- createDataPartition(final_df$preciptype, p = p_values[i], list = FALSE)
train_data <- final_df[train_idx,]
test_data <- final_df[-train_idx,]
}
# Choose the p value with the best performance
best_p <- p_values[which.max(results)]
best_p## [1] 0.64.2 To find which ntree value and accuracy are suits for this dataset - Grid Search
ntree_values <- c(50, 100, 150, 200, 250, 300, 350, 400, 450, 500)
top_n_options <- 10 # Number of top options to keep track of4.3 Initialize lists to store results and ntree values
results <- numeric(length(ntree_values))
top_n_results <- numeric(top_n_options)
top_n_ntrees <- numeric(top_n_options)4.4 Data Split
train_idx <- createDataPartition(final_df$preciptype, p = 0.6, list = FALSE)
train_data <- final_df[train_idx,]
test_data <- final_df[-train_idx,]4.5 Grid search loop
for (i in seq_along(ntree_values)) {
classifier_RF <- randomForest(
x = subset(train_data, select = -c(preciptype)),
y = as.factor(train_data$preciptype),
ntree = ntree_values[i],
importance = TRUE
)
# Make predictions on the test set
preds <- predict(classifier_RF, newdata = subset(test_data, select = -c(preciptype)))
# Calculate accuracy
conf_mat <- table(preds, test_data$preciptype)
accuracy <- sum(diag(conf_mat)) / sum(conf_mat)
results[i] <- accuracy
}4.6 Find the top n options
top_n_indices <- order(results, decreasing = TRUE)[1:top_n_options]
top_n_ntrees <- ntree_values[top_n_indices]
top_n_results <- results[top_n_indices]4.7 Print the top n options
for (i in seq_along(top_n_ntrees)) {
cat("Option", i, ": ntree =", top_n_ntrees[i], "Accuracy =", top_n_results[i], "\n")
}## Option 1 : ntree = 400 Accuracy = 0.8437253
## Option 2 : ntree = 200 Accuracy = 0.8431992
## Option 3 : ntree = 150 Accuracy = 0.8424099
## Option 4 : ntree = 350 Accuracy = 0.8421468
## Option 5 : ntree = 500 Accuracy = 0.8418837
## Option 6 : ntree = 50 Accuracy = 0.8410944
## Option 7 : ntree = 250 Accuracy = 0.8410944
## Option 8 : ntree = 300 Accuracy = 0.8405683
## Option 9 : ntree = 450 Accuracy = 0.8405683
## Option 10 : ntree = 100 Accuracy = 0.8384636Explanation： The more trees there are in a random forest, the higher the complexity of the model. Increasing the number of trees may improve the model’s fit to the training data, but may also lead to overfitting on the test data. “ntree = 100” was chosen to achieve reasonable performance while keeping the model relatively simple, thus avoiding overfitting.
5 Development
5.1 Write a table forcast to file
# Tata frame called `table_forecast`
library(writexl)
# Write the data frame to an Excel file
write_xlsx(table_forecast, "/Users/hexiaofeng/Downloads/WQD7004_Group Assignment/weather_forecasting_data.xlsx")5.2 Write a outcome to file
# Tata frame called `outcome_df`
library(writexl)
# Write the data frame to an Excel file
write_xlsx(outcome_df, "/Users/hexiaofeng/Downloads/WQD7004_Group Assignment/weather_forecasting_data.xlsx")5.3 Rshiny interfaces
The Shiny app plays a key role in this project, providing users with an interactive data product covering past weather conditions from March 2022 to March 2023, with forecasts for the following week, 2023 Weather forecast for April 2019. The app’s dashboard is designed to be user-friendly, allowing users to easily select dates of interest and customize the weather data they want to know about.
On the dashboard, users can view weather conditions for a specific time period by selecting the year, month, and specific date. Once the user makes a selection, the dashboard will present detailed weather information, including but not limited to temperature, dew, wind speed and other indicators. This gives users a comprehensive view of weather conditions for a selected date, whether looking back at past data or predicting future weather.
6 Conclusion
The Kuala Lumpur weather data was obtained and gathered first before cleaning. The irrelevant and null columns were remcved and the columns with minimal missing values were imputed accordingly. Then, via exploratory data analysis, the data was further cleaned by removing the columns with highly carrelated variables. Violin and bar plots were done to wisualize the distribution and the measures of dispersion of the data of each variable.
Then, the cleaned data was used to train the random forest classifier. The importance of each feature in predicting the target wariables were calculated.The accuracy of the classifier was calculated. After removing “severerisk”, whiich is the variable that has the least impact on the classifier’s prediction, the classifier was trained again, The accuracy of the classilfier improved from 0.833 to 0.837.
Then, the important features were used in time series analysis to forecast their respective values in the next following week.ARIMA model was used in time series analysis. The ARIMA model was used to forecast the weather conditions for next one week with hourly intervals, which corresponds to 168 predictions. The accuracy of each feature in the ARIMA model were evaluated.
The data forecasted by ARIMA model was used as the predictor wariables in the RF model to predict the weather conditions, that is whether rain or no rain, in the next 168 hours (7 days). The prediction was then written to an excel file to be used in the deployment of the weather prediction data product.
The data product that we developed was a Shiny application for the end-users The application allows users to select the year, month and date, and the dashboard will present all of the weather canditions, including temperature, dew, windspeed and etc.
in the future, we could wark an performing prescriptive analysis in order to improve the economy of various weather-dependent industries such as tourism, agriculture and renewable energy. .We could try utilizing k-Nearest Neighbers and Decision Tree algorithms in order to develop data products that can recommend climate-based solurtions to improve execution of tourism promation, improve quality and volume of crop yields cr improve generation of solar ar hydro electricity,
---
title: "WQD7004 - Group Project - Weather Forecasting"
output:
  bookdown::html_document2:
    toc: true
    toc_float:
      collapsed: false
      smooth_scroll: true
    theme: united
    code_folding: show
    code_download: true
toc_depth: 3
---

<span style="color:orange">Group Members</span>  
1.  22099247 Wang Youqing  
2.  22070924 He Xiaofeng  
3.  S2176972 Muhammad Nasrullah  
4.  S2169356 Nur Izzah Athirah Alzahri  
5.  22056889 Rayan Omar Abuhasha  


# Data Understanding
## Introduction

Kuala Lumpur, the capital and geographic centre of Malaysia, is a thriving metropolis that sees a daily influx of numerous economic activity. Even though Kuala Lumpur is a very cosmopolitan city, it is but not immune to the many, erratic natural disasters that, among other things, bring down the spirits of countless industrious Malaysians through torrential downpours, typhoons, and flash floods. The government organisation in charge of weather forecasting in this nation is the Malaysian Meteorological Department, or MET Malaysia. Over the past ten years, meteorology has become increasingly data-centric, necessitating the need for more advanced techniques for analysing and interpreting weather data. Complex machine learning approaches are preferred over their traditional counterparts to handle this new data boom, as they are better able to capture the relationships between different weather variables in situations where there is a large amount of data, which can be difficult with traditional methods. It appears that a large number of research papers have used machine learning simulations for weather forecasting, but very few have produced a useful data result. The main motivation for this group project is the requirement to create a data product that can reliably predict and forecast the weather in Kuala Lumpur, Malaysia. weather prediction and forecasting are crucial aspects of daily life, more so in areas where weather patterns are highly unpredictable such as in Kuala Lumpur, Malaysia.Accurate and timely predictions of weather conditions are essential for a range of economic and non-economic activities. However, despite advances in weather forecasting technology, predicting weather with high accuracy remains a challenging problem to this present day, but a problem we wish to tackle in this group project.

## Project Objective

1. To Develop an Accurate Rainfall Prediction Model

2. To Evaluate Model Stability and Accuracy

3. To Provide Practical Forecasts for Users

4. To Create a Shiny Data Product for Instant Weather Forecasts

## Data Background

-   Title: weather_forecasting_data

-   Year: 2022-2023

-   Purpose: forecast weather conditions in Kuala Lumpur

-   Source: <https://www.visualcrossing.com/weather/weather-data-services/kuala%20lumpur>


## Import the packages before starting with data preprocessing
```{r, echo=FALSE}
library(readxl)
library(dplyr)
```

## Read the original dataset

```{r}
origenal_data <- read_excel("/Users/hexiaofeng/Downloads/WQD7004_Group Assignment/weather_forecasting_data.xlsx")
head(origenal_data)
```

## Load all library

```{r message=FALSE, warning=FALSE}
library(pryr)
library(Rcpp)
library(ggplot2)
library(lubridate)
library(tidyr)
library(caret)
library(randomForest)
library(devtools)
library(infotheo)
library(corrplot)
library(reshape2)
library(coefplot)
library(forecast)
library(writexl)
library(tseries)
```

```{r, echo=FALSE}
set.seed(123)


# Function
data_profiling = function(x) {
  
  #visualize
  #numeric = violin plot, char = horizontal bar plot
  
  for (col_name in colnames(x)) {
    
    if (is.numeric(x[[col_name]])) {
      
      p = ggplot(x, aes(x = col_name, y = x[[col_name]], fill = col_name)) +
        geom_violin() +
        labs(x = "Column", y = "Range", title = paste("Plot of", col_name)) +
        theme_minimal()
      print(p)
      
    }
    
    else if (is.character(x[[col_name]])) {
      counts = table(x[[col_name]])
      df_counts = data.frame(category = names(counts), count = as.numeric(counts))
      p = ggplot(df_counts, aes(x = count, y = category)) +
        geom_col(fill = "steelblue") +
        labs(x = "Count", y = "Category", title = col_name) +
        theme_minimal()
      print(p)
    }
  }  
  
}

get_upper_tri = function(cormat){
  
  #Get upper triangle of the correlation matrix
  cormat[lower.tri(cormat)]= NA
  return(cormat)
}
```
Load data
```{r}

excel_file = "/Users/hexiaofeng/Downloads/WQD7004_Group Assignment/weather_forecasting_data.xlsx"

# get the names of all sheets in the file
sheet_names = excel_sheets(excel_file)
```

# Data Preparation

## Create an empty data frame to store the combined data

```{r}
combined_data = data.frame()
```

## Loop through all sheets and combine them into the data frame

```{r}
for (sheet in sheet_names) {
  sheet_data = read_excel(excel_file, sheet = sheet)
  
  print(sheet)
  
  sheet_data$datetime <- gsub("T", " ", sheet_data$datetime)
  sheet_data$datetime <- as.POSIXct(sheet_data$datetime, format = "%Y-%m-%d %H:%M:%S")
  sheet_data$temp <- as.numeric(sheet_data$temp)
  sheet_data$feelslike <- as.numeric(sheet_data$feelslike)
  sheet_data$dew <- as.numeric(sheet_data$dew)
  sheet_data$humidity <- as.numeric(sheet_data$humidity)
  sheet_data$precip <- as.numeric(sheet_data$precip)
  sheet_data$precipprob <- as.numeric(sheet_data$precipprob)
  sheet_data$snow <- as.numeric(sheet_data$snow)
  sheet_data$snowdepth <- as.numeric(sheet_data$snowdepth)
  sheet_data$windgust <- as.numeric(sheet_data$windgust)
  sheet_data$windspeed <- as.numeric(sheet_data$windspeed)
  sheet_data$winddir <- as.numeric(sheet_data$winddir)
  sheet_data$sealevelpressure <- as.numeric(sheet_data$sealevelpressure)
  sheet_data$cloudcover <- as.numeric(sheet_data$cloudcover)
  sheet_data$visibility <- as.numeric(sheet_data$visibility)
  sheet_data$solarradiation <- as.numeric(sheet_data$solarradiation)
  sheet_data$solarenergy <- as.numeric(sheet_data$solarenergy)
  sheet_data$uvindex <- as.numeric(sheet_data$uvindex)
  sheet_data$severerisk <- as.numeric(sheet_data$severerisk)
  
  combined_data = bind_rows(combined_data, sheet_data)
}
```
## Drop not make sense columns 

```{r}
dataframe = subset(combined_data, select = -c(name, conditions, icon, stations))
```

##  View the content of the data 

```{r}
names(dataframe)
object_size(dataframe)
str(dataframe)
summary(dataframe)
```

##  Check data types, distribution, weird number 

```{r, warning=FALSE}
data_profiling(dataframe)
```

##  Not useful (cant use to differentiate preciptype), drop 
As we can see from the plot above, the snow and snowdepth columns have low correlations with other variables and are unlikely to have a significant impact on the performance of the model. So drop it

```{r}
dataframe = subset(dataframe, select = -c(snow, snowdepth))
```

##  Check na occurrence 

```{r, echo=FALSE, warning=FALSE}
null_counts = colSums(is.na(dataframe))
print(null_counts)
par(las =1, mar = c(5, 10, 4, 2) + 0.1)
barplot(null_counts, main = "Null Value Counts by Column", xlab = "Null Value Counts", horiz = TRUE)
```

##  Less than 10%, replace all 

  - Missing values in the 'solarradiation' column are replaced with the mean of non-missing values in column.
	- Missing values in the 'uvindex' column are replaced with the mean of non-missing values in that column.
	- Missing values in the 'preciptype' column are replaced with the string "no rain."


```{r}
dataframe$solarradiation = replace(dataframe$solarradiation, is.na(dataframe$solarradiation), mean(dataframe$solarradiation, na.rm = TRUE))
dataframe$uvindex = replace(dataframe$uvindex, is.na(dataframe$uvindex), mean(dataframe$uvindex, na.rm = TRUE))
dataframe$preciptype = replace(dataframe$preciptype, is.na(dataframe$preciptype), "no rain")

```

##  Too much null, drop 

```{r}
dataframe = subset(dataframe, select = -c(solarenergy))
```

##  Check na occurrence again 

```{r}
null_counts=colSums(is.na(dataframe))
print(null_counts)
```

## Re-check

```{r}
print(unique(dataframe$preciptype))
print(str(dataframe))
```

## Feature selection 

Correlation check for non categorical and plot heatmap

```{r, echo=FALSE, warning=FALSE}
test_df = subset(dataframe, select = -c(preciptype, datetime))

cormat = round(cor(test_df),2)
upper_tri = get_upper_tri(cormat)
melted_cormat = melt(upper_tri, na.rm = TRUE)
ggplot(data = melted_cormat, aes(Var2, Var1, fill = value))+
  geom_tile(color = "white")+
  scale_fill_gradient2(low = "blue", high = "red", mid = "white", 
                       midpoint = 0, limit = c(-1,1), space = "Lab", 
                       name="Pearson\nCorrelation") +
  theme_minimal()+ 
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))+
  coord_fixed()
```

## Drop high correlation column
The main purpose of removing highly correlated variables is to avoid multicollinearity.
Therefore, in order to improve the interpretability, stability and performance of the model, we choose to delete highly correlated variables. During the feature selection process, selecting to retain features that have a high correlation with the target variable but a low correlation between independent variables helps to build a more reliable model.

```{r}
final_df_with_datetime = subset(dataframe, select = -c(feelslike, uvindex, humidity)) 
final_df = subset(final_df_with_datetime, select =-c(datetime))

print(colnames(final_df))
```
# Modeling

## Data Split
```{r}
train_idx = createDataPartition(final_df$preciptype, p = 0.6, list =FALSE)
train_data = final_df[train_idx,] 
test_data = final_df[-train_idx,]
```
## Random Forest Classifier

### Train the random forest classifier
```{r}
classifier_RF = randomForest(x = subset(train_data, select = -c(preciptype)), y =
as.factor(train_data$preciptype), ntree = 100, importance = TRUE)
```
### Make predictions on the test data

```{r}
preds = predict(classifier_RF,
newdata = subset(test_data, select = -c(preciptype)))
```

###  Calculate the confusion matrix/ accuracy of the predictions 

```{r}
conf_mat = table(preds, test_data$preciptype)
accuracy = sum(diag(conf_mat)) / sum(conf_mat)
print(conf_mat)
print(paste("Accuracy:", round(accuracy, 3)))
```
###  Explanation ：Confusion matrix: 
	- True Positives (TP): 276 (Actual Rain, Predicted Rain)
	- True Negatives (TN): 2912 (Actual No Rain, Predicted No Rain)
	- False Positives (FP): 529 (Actual No Rain, Predicted Rain)
	- False Negatives (FN): 84 (Actual Rain, Predicted No Rain)

Accuracy: The accuracy of the model is calculated as the sum of correctly predicted instances (TP + TN) divided by the total number of instances. In this case, the accuracy is approximately 83.9%.

Evaluation: The Random Forest Classifier shows good predictive performance with an accuracy of 83.9%. It effectively identifies both rainy and non-rainy instances. However, it is important to consider other metrics like precision, recall, and F1 score, especially when dealing with imbalanced datasets. These additional metrics provide a more comprehensive understanding of the classifier's performance, especially in scenarios where certain classes may be underrepresented.

###  Feature important 

```{r}
imp = importance(classifier_RF)
print(imp)
varImpPlot(classifier_RF)
```

###  Classification 

###  After feature important done 

```{r}
final_df = subset(final_df_with_datetime, select = -c(datetime, severerisk))


# Split the data into training and testing sets
train_idx = createDataPartition(final_df$preciptype, p = 0.6, list = FALSE)
train_data = final_df[train_idx,]

test_data = final_df[-train_idx,]

#Train the random forest classifier
final_classifier_RF = randomForest(x = subset(train_data, select = -c(preciptype)),
                             y = as.factor(train_data$preciptype),
                             ntree = 100,
                             importance = TRUE)
```

###  Make predictions on the test data 

```{r}
preds = predict(final_classifier_RF, newdata = subset(test_data, select = -c(preciptype)))
```

###  Calculate the confusion matrix/ accuracy of the predictions 

```{r}
conf_mat = table(preds, test_data$preciptype)
accuracy = sum(diag(conf_mat)) / sum(conf_mat)
print(conf_mat)
print(paste("Accuracy:", round(accuracy, 3)))
```

###  Feature important 

```{r}
imp = importance(final_classifier_RF)
print(imp)
varImpPlot(final_classifier_RF)
```

##  Time Series 

###  Time series prediction 

```{r}
final_df = subset(final_df_with_datetime, select = -c(severerisk))
names(final_df)

final_df$datetime <- seq.POSIXt(from = as.POSIXct("2022-03-01 00:00:00"), length.out = 9504, by = "60 mins")
head(final_df) 
```

###  ts() is time series function 

```{r}
data_temp <- ts(final_df$temp, start = decimal_date(ymd_hms("2022-03-01 00:00:00 UTC")), frequency = 9504)
data_dew <- ts(final_df$dew, start = decimal_date(ymd_hms("2022-03-01 00:00:00 UTC")), frequency = 9504)
data_precip <- ts(final_df$precip, start = decimal_date(ymd_hms("2022-03-01 00:00:00 UTC")), frequency = 9504)
data_precipprob <- ts(final_df$precipprob, start = decimal_date(ymd_hms("2022-03-01 00:00:00 UTC")), frequency = 9504)
data_windgust <- ts(final_df$windgust, start = decimal_date(ymd_hms("2022-03-01 00:00:00 UTC")), frequency = 9504)
data_windspeed <- ts(final_df$windspeed, start = decimal_date(ymd_hms("2022-03-01 00:00:00 UTC")), frequency = 9504)
data_winddir <- ts(final_df$winddir, start = decimal_date(ymd_hms("2022-03-01 00:00:00 UTC")), frequency = 9504)
data_sealevelpressure <- ts(final_df$sealevelpressure, start = decimal_date(ymd_hms("2022-03-01 00:00:00 UTC")), frequency = 9504)
data_cloudcover <- ts(final_df$cloudcover, start = decimal_date(ymd_hms("2022-03-01 00:00:00 UTC")), frequency = 9504)
data_visibility <- ts(final_df$visibility, start = decimal_date(ymd_hms("2022-03-01 00:00:00 UTC")), frequency = 9504)
data_solarradiation <- ts(final_df$solarradiation, start = decimal_date(ymd_hms("2022-03-01 00:00:00 UTC")), frequency = 9504)
```

###  Use auto.arima() to get optimal auto ARIMA Model 

```{r}
autoarimal_temp <- auto.arima(data_temp)
autoarimal_temp
autoarimal_dew <- auto.arima(data_dew)
autoarimal_precip <- auto.arima(data_precip)
autoarimal_precipprob <- auto.arima(data_precipprob)
autoarimal_windgust <- auto.arima(data_windgust)
autoarimal_windspeed <- auto.arima(data_windspeed)
autoarimal_winddir <- auto.arima(data_winddir)
autoarimal_sealevelpressure <- auto.arima(data_sealevelpressure)
autoarimal_cloudcover <- auto.arima(data_cloudcover)
autoarimal_visibility <- auto.arima(data_visibility)
autoarimal_solarradiation <- auto.arima(data_solarradiation)
```

###  Show the forecast data for 1 Week each hour (7 Day x 24 Hours = 168) 

```{r}
predict_temp <- forecast(autoarimal_temp, h = 168)
predict_dew <- forecast(autoarimal_dew, h = 168)
predict_precip <- forecast(autoarimal_precip, h = 168)
predict_precipprob <- forecast(autoarimal_precipprob, h = 168)
predict_windgust <- forecast(autoarimal_windgust, h = 168)
predict_windspeed <- forecast(autoarimal_windspeed, h = 168)
predict_winddir <- forecast(autoarimal_winddir, h = 168)
predict_sealevelpressure <- forecast(autoarimal_sealevelpressure, h = 168)
predict_cloudcover <- forecast(autoarimal_cloudcover, h = 168)
predict_visibility <- forecast(autoarimal_visibility, h = 168)
predict_solarradiation <- forecast(autoarimal_solarradiation, h = 168)

options(max.print = 10000)

head(predict_temp)
head(predict_dew)
head(predict_precip)
head(predict_precipprob)
head(predict_windgust)
head(predict_windspeed)
head(predict_winddir)
head(predict_sealevelpressure)
head(predict_cloudcover)
head(predict_visibility)
head(predict_solarradiation)
```

###  Combine all forecast data 

```{r}
table_forecast <- subset(data.frame(
  predict_temp, predict_dew, predict_precip, predict_precipprob,
  predict_windgust, predict_windspeed, predict_winddir, 
  predict_sealevelpressure, predict_cloudcover, predict_visibility, 
  predict_solarradiation))
table_forecast <- subset(data.frame(
  predict_temp, predict_dew, predict_precip, predict_precipprob,
  predict_windgust, predict_windspeed, predict_winddir, 
  predict_sealevelpressure, predict_cloudcover, predict_visibility, 
  predict_solarradiation), 
  select = -c(Lo.80, Hi.80, Lo.95, Hi.95, Lo.80.1, Hi.80.1, Lo.95.1, Hi.95.1,
              Lo.80.2, Hi.80.2, Lo.95.2, Hi.95.2, Lo.80.3, Hi.80.3, Lo.95.3, Hi.95.3,
              Lo.80.4, Hi.80.4, Lo.95.4, Hi.95.4, Lo.80.5, Hi.80.5, Lo.95.5, Hi.95.5,
              Lo.80.6, Hi.80.6, Lo.95.6, Hi.95.6, Lo.80.7, Hi.80.7, Lo.95.7, Hi.95.7,
              Lo.80.8, Hi.80.8, Lo.95.8, Hi.95.8, Lo.80.9, Hi.80.9, Lo.95.9, Hi.95.9,
              Lo.80.10, Hi.80.10, Lo.95.10, Hi.95.10))

colnames(table_forecast)  <- c("temp", "dew", "precip", "precipprob",
                               "windgust", "windspeed", "winddir", 
                               "sealevelpressure", "cloudcover", "visibility", 
                               "solarradiation")
```

###  DateTime 

```{r}
dtimes = seq.POSIXt(from = as.POSIXct("2023-04-01 00:00:00"), length.out = 168, by = "60 mins")
dtimes
table_forecast$time_column <- row.names(table_forecast)
row.names(table_forecast) <- dtimes
table_forecast
table_forecast = subset(table_forecast, select = -c(time_column))
head(table_forecast)
```

###  Combine Time Series with Classification 

```{r}
print(table_forecast)
final_preds = predict(final_classifier_RF, newdata = table_forecast)
head(final_preds)

outcome_df = data.frame(final_preds)

outcome_df$time_column <- row.names(outcome_df)
row.names(outcome_df) <- dtimes
head(outcome_df)

outcome_df$time_column <- row.names(outcome_df)
row.names(outcome_df) <- NULL
head(outcome_df)
```

# Evaluation 

## To find which p value (training set) are suitable. p = 0.6  - Cross-Validation 

```{r}
p_values <- c(0.6, 0.7, 0.8)
results <- numeric(length(p_values))

for (i in seq_along(p_values)) {
  train_idx <- createDataPartition(final_df$preciptype, p = p_values[i], list = FALSE)
  train_data <- final_df[train_idx,]
  test_data <- final_df[-train_idx,]
}

# Choose the p value with the best performance
best_p <- p_values[which.max(results)]
best_p
```

##  To find which ntree value and accuracy are suits for this dataset - Grid Search 

```{r}
ntree_values <- c(50, 100, 150, 200, 250, 300, 350, 400, 450, 500)
top_n_options <- 10  # Number of top options to keep track of
```

##  Initialize lists to store results and ntree values 

```{r}
results <- numeric(length(ntree_values))
top_n_results <- numeric(top_n_options)
top_n_ntrees <- numeric(top_n_options)
```

```{r echo=FALSE}
set.seed(123)
```

##  Data Split 

```{r}
train_idx <- createDataPartition(final_df$preciptype, p = 0.6, list = FALSE)
train_data <- final_df[train_idx,]
test_data <- final_df[-train_idx,]
```

##  Grid search loop 

```{r}
for (i in seq_along(ntree_values)) {
  classifier_RF <- randomForest(
    x = subset(train_data, select = -c(preciptype)),
    y = as.factor(train_data$preciptype),
    ntree = ntree_values[i],
    importance = TRUE
  )
  
  # Make predictions on the test set
  preds <- predict(classifier_RF, newdata = subset(test_data, select = -c(preciptype)))
  
  # Calculate accuracy
  conf_mat <- table(preds, test_data$preciptype)
  accuracy <- sum(diag(conf_mat)) / sum(conf_mat)
  
  results[i] <- accuracy
}
```

##  Find the top n options 

```{r}
top_n_indices <- order(results, decreasing = TRUE)[1:top_n_options]
top_n_ntrees <- ntree_values[top_n_indices]
top_n_results <- results[top_n_indices]
```

##  Print the top n options 

```{r}
for (i in seq_along(top_n_ntrees)) {
  cat("Option", i, ": ntree =", top_n_ntrees[i], "Accuracy =", top_n_results[i], "\n")
}
```
Explanation：
The more trees there are in a random forest, the higher the complexity of the model. Increasing the number of trees may improve the model's fit to the training data, but may also lead to overfitting on the test data. "ntree = 100" was chosen to achieve reasonable performance while keeping the model relatively simple, thus avoiding overfitting.


#  Development
##  Write a table forcast to file 

```{r}
# Tata frame called `table_forecast`
library(writexl)
# Write the data frame to an Excel file
write_xlsx(table_forecast, "/Users/hexiaofeng/Downloads/WQD7004_Group Assignment/weather_forecasting_data.xlsx")

```
##  Write a outcome to file 
```{r}
# Tata frame called `outcome_df`
library(writexl)
# Write the data frame to an Excel file
write_xlsx(outcome_df, "/Users/hexiaofeng/Downloads/WQD7004_Group Assignment/weather_forecasting_data.xlsx")

```

## Rshiny interfaces

The Shiny app plays a key role in this project, providing users with an interactive data product covering past weather conditions from March 2022 to March 2023, with forecasts for the following week, 2023 Weather forecast for April 2019. The app's dashboard is designed to be user-friendly, allowing users to easily select dates of interest and customize the weather data they want to know about.

On the dashboard, users can view weather conditions for a specific time period by selecting the year, month, and specific date. Once the user makes a selection, the dashboard will present detailed weather information, including but not limited to temperature, dew, wind speed and other indicators. This gives users a comprehensive view of weather conditions for a selected date, whether looking back at past data or predicting future weather.


# Conclusion

The Kuala Lumpur weather data was obtained and gathered first before cleaning. The irrelevant and null columns were remcved and the columns with minimal missing values were imputed accordingly. Then, via exploratory data analysis, the data was further cleaned by removing the columns with highly carrelated variables. Violin and bar plots were done to wisualize the distribution and the measures of dispersion of the data of each variable.

Then, the cleaned data was used to train the random forest classifier. The importance of each feature in predicting the target wariables were calculated.The accuracy of the classifier was calculated. After removing "severerisk", whiich is the variable that has the least impact on the classifier's prediction, the classifier was trained again, The accuracy of the classilfier improved from 0.833 to 0.837.

Then, the important features were used in time series analysis to forecast their respective values in the next following week.ARIMA model was used in time series analysis. The ARIMA model was used to forecast the weather conditions for next one week with hourly intervals, which corresponds to 168 predictions. The accuracy of each feature in the ARIMA model were evaluated.

The data forecasted by ARIMA model was used as the predictor wariables in the RF model to predict the weather conditions, that is whether rain or no rain, in the next 168 hours (7 days). The prediction was then written to an excel file to be used in the deployment of the weather prediction data product.

The data product that we developed was a Shiny application for the end-users The application allows users to select the year, month and date, and the dashboard will present all of the weather canditions, including temperature, dew, windspeed and etc.

 in the future, we could wark an performing prescriptive analysis in order to improve the economy of various weather-dependent industries such as tourism, agriculture and renewable energy.
.We could try utilizing k-Nearest Neighbers and Decision Tree algorithms in order to develop data products that can recommend climate-based solurtions to improve execution of tourism promation, improve quality and volume of crop yields cr improve generation of solar ar hydro electricity,
