South ural state university, Chelyabinsk, Russian federation
#Import
library(fpp2)
## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
## -- Attaching packages ---------------------------------------------- fpp2 2.4 --
## v ggplot2 3.3.2 v fma 2.4
## v forecast 8.13 v expsmooth 2.3
##
library(forecast)
library(ggplot2)
library("readxl")
library(moments)
library(forecast)
require(forecast)
require(tseries)
## Loading required package: tseries
require(markovchain)
## Loading required package: markovchain
## Package: markovchain
## Version: 0.8.5-3
## Date: 2020-12-03
## BugReport: https://github.com/spedygiorgio/markovchain/issues
require(data.table)
## Loading required package: data.table
library(Hmisc)
## Loading required package: lattice
## Loading required package: survival
## Loading required package: Formula
##
## Attaching package: 'Hmisc'
## The following objects are masked from 'package:base':
##
## format.pval, units
library(ascii)
##Global vriable##
Full_original_data <- read.csv("data.csv") # path of your data ( time series data)
original_data<-Full_original_data$infection
y_lab <- "Forecast Third wave infection cases in Chelyabinsk" # input name of data
Actual_date_interval <- c("2020/03/12","2021/06/17")
Forecast_date_interval <- c("2021/06/18","2021/07/30")
validation_data_days <-7
frequency<-"days"
Number_Neural<-15 # Number of Neural For model NNAR Model
NNAR_Model<- FALSE #create new model (TRUE/FALSE)
frequency<-"days"
# Data Preparation & calculate some of statistics measures
summary(original_data) # Summary your time series
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 79.0 116.0 136.7 185.0 317.0
# calculate standard deviation
data.frame(kurtosis=kurtosis(original_data)) # calculate Cofficient of kurtosis
## kurtosis
## 1 2.41796
data.frame(skewness=skewness(original_data)) # calculate Cofficient of skewness
## skewness
## 1 0.5932153
data.frame(Standard.deviation =sd(original_data))
## Standard.deviation
## 1 88.75785
#processing on data (input data)
rows <- NROW(original_data) # calculate number of rows in time series (number of days)
training_data<-original_data[1:(rows-validation_data_days)] # Training data
testing_data<-original_data[(rows-validation_data_days+1):rows] #testing data
AD<-fulldate<-seq(as.Date(Actual_date_interval[1]),as.Date(Actual_date_interval[2]), frequency) #input range for actual date
FD<-seq(as.Date(Forecast_date_interval[1]),as.Date(Forecast_date_interval[2]), frequency) #input range forecasting date
N_forecasting_days<-nrow(data.frame(FD)) #calculate number of days that you want to forecasting
validation_dates<-tail(AD,validation_data_days) # select validation_dates
validation_data_by_name<-weekdays(validation_dates) # put names of validation dates
forecasting_data_by_name<-weekdays(FD) # put names of Forecasting dates
#NNAR Model
if(NNAR_Model==TRUE){
data_series<-ts(training_data)
model_NNAR<-nnetar(data_series, size = Number_Neural)
saveRDS(model_NNAR, file = "model_NNAR.RDS")
my_model <- readRDS("model_NNAR.RDS")
accuracy(model_NNAR) # accuracy on training data #Print Model Parameters
model_NNAR
}
if(NNAR_Model==FALSE){
data_series<-ts(training_data)
#model_NNAR<-nnetar(data_series, size = Number_Numeral)
model_NNAR <- readRDS("model_NNAR.RDS")
accuracy(model_NNAR) # accuracy on training data #Print Model Parameters
model_NNAR
}
## Series: data_series
## Model: NNAR(19,15)
## Call: nnetar(y = data_series, size = Number_Neural)
##
## Average of 20 networks, each of which is
## a 19-15-1 network with 316 weights
## options were - linear output units
##
## sigma^2 estimated as 12.48
# Testing Data Evaluation
forecasting_NNAR <- forecast(model_NNAR, h=N_forecasting_days+validation_data_days)
validation_forecast<-head(forecasting_NNAR$mean,validation_data_days)
MAPE_Per_Day<-round( abs(((testing_data-validation_forecast)/testing_data)*100) ,3)
paste ("MAPE % For ",validation_data_days,frequency,"by using NNAR Model for ==> ",y_lab, sep=" ")
## [1] "MAPE % For 7 days by using NNAR Model for ==> Forecast Third wave infection cases in Chelyabinsk"
MAPE_Mean_All<-paste(round(mean(MAPE_Per_Day),3),"% MAPE ",validation_data_days,frequency,y_lab,sep=" ")
MAPE_Mean_All_NNAR<-round(mean(MAPE_Per_Day),3)
MAPE_NNAR<-paste(round(MAPE_Per_Day,3),"%")
MAPE_NNAR_Model<-paste(MAPE_Per_Day ,"%")
paste (" MAPE that's Error of Forecasting for ",validation_data_days," days in NNAR Model for ==> ",y_lab, sep=" ")
## [1] " MAPE that's Error of Forecasting for 7 days in NNAR Model for ==> Forecast Third wave infection cases in Chelyabinsk"
paste(MAPE_Mean_All,"%")
## [1] "0.955 % MAPE 7 days Forecast Third wave infection cases in Chelyabinsk %"
paste ("MAPE that's Error of Forecasting day by day for ",validation_data_days," days in NNAR Model for ==> ",y_lab, sep=" ")
## [1] "MAPE that's Error of Forecasting day by day for 7 days in NNAR Model for ==> Forecast Third wave infection cases in Chelyabinsk"
print(ascii(data.frame(date_NNAR=validation_dates,validation_data_by_name,actual_data=testing_data,forecasting_NNAR=validation_forecast,MAPE_NNAR_Model)), type = "rest")
##
## +---+------------+-------------------------+-------------+------------------+-----------------+
## | | date_NNAR | validation_data_by_name | actual_data | forecasting_NNAR | MAPE_NNAR_Model |
## +===+============+=========================+=============+==================+=================+
## | 1 | 2021-06-11 | Friday | 86.00 | 86.82 | 0.958 % |
## +---+------------+-------------------------+-------------+------------------+-----------------+
## | 2 | 2021-06-12 | Saturday | 87.00 | 87.27 | 0.31 % |
## +---+------------+-------------------------+-------------+------------------+-----------------+
## | 3 | 2021-06-13 | Sunday | 87.00 | 87.71 | 0.812 % |
## +---+------------+-------------------------+-------------+------------------+-----------------+
## | 4 | 2021-06-14 | Monday | 88.00 | 88.86 | 0.978 % |
## +---+------------+-------------------------+-------------+------------------+-----------------+
## | 5 | 2021-06-15 | Tuesday | 88.00 | 89.57 | 1.786 % |
## +---+------------+-------------------------+-------------+------------------+-----------------+
## | 6 | 2021-06-16 | Wednesday | 89.00 | 90.26 | 1.414 % |
## +---+------------+-------------------------+-------------+------------------+-----------------+
## | 7 | 2021-06-17 | Thursday | 92.00 | 91.61 | 0.424 % |
## +---+------------+-------------------------+-------------+------------------+-----------------+
print(ascii(data.frame(FD,forecating_date=forecasting_data_by_name,forecasting_by_NNAR=tail(forecasting_NNAR$mean,N_forecasting_days))), type = "rest")
##
## +----+------------+-----------------+---------------------+
## | | FD | forecating_date | forecasting_by_NNAR |
## +====+============+=================+=====================+
## | 1 | 2021-06-18 | Friday | 93.04 |
## +----+------------+-----------------+---------------------+
## | 2 | 2021-06-19 | Saturday | 94.09 |
## +----+------------+-----------------+---------------------+
## | 3 | 2021-06-20 | Sunday | 95.70 |
## +----+------------+-----------------+---------------------+
## | 4 | 2021-06-21 | Monday | 97.53 |
## +----+------------+-----------------+---------------------+
## | 5 | 2021-06-22 | Tuesday | 99.16 |
## +----+------------+-----------------+---------------------+
## | 6 | 2021-06-23 | Wednesday | 100.86 |
## +----+------------+-----------------+---------------------+
## | 7 | 2021-06-24 | Thursday | 103.40 |
## +----+------------+-----------------+---------------------+
## | 8 | 2021-06-25 | Friday | 105.78 |
## +----+------------+-----------------+---------------------+
## | 9 | 2021-06-26 | Saturday | 107.87 |
## +----+------------+-----------------+---------------------+
## | 10 | 2021-06-27 | Sunday | 110.62 |
## +----+------------+-----------------+---------------------+
## | 11 | 2021-06-28 | Monday | 113.95 |
## +----+------------+-----------------+---------------------+
## | 12 | 2021-06-29 | Tuesday | 117.17 |
## +----+------------+-----------------+---------------------+
## | 13 | 2021-06-30 | Wednesday | 120.53 |
## +----+------------+-----------------+---------------------+
## | 14 | 2021-07-01 | Thursday | 124.46 |
## +----+------------+-----------------+---------------------+
## | 15 | 2021-07-02 | Friday | 128.74 |
## +----+------------+-----------------+---------------------+
## | 16 | 2021-07-03 | Saturday | 133.02 |
## +----+------------+-----------------+---------------------+
## | 17 | 2021-07-04 | Sunday | 137.81 |
## +----+------------+-----------------+---------------------+
## | 18 | 2021-07-05 | Monday | 143.08 |
## +----+------------+-----------------+---------------------+
## | 19 | 2021-07-06 | Tuesday | 148.49 |
## +----+------------+-----------------+---------------------+
## | 20 | 2021-07-07 | Wednesday | 154.26 |
## +----+------------+-----------------+---------------------+
## | 21 | 2021-07-08 | Thursday | 160.50 |
## +----+------------+-----------------+---------------------+
## | 22 | 2021-07-09 | Friday | 166.84 |
## +----+------------+-----------------+---------------------+
## | 23 | 2021-07-10 | Saturday | 173.07 |
## +----+------------+-----------------+---------------------+
## | 24 | 2021-07-11 | Sunday | 179.65 |
## +----+------------+-----------------+---------------------+
## | 25 | 2021-07-12 | Monday | 186.08 |
## +----+------------+-----------------+---------------------+
## | 26 | 2021-07-13 | Tuesday | 191.69 |
## +----+------------+-----------------+---------------------+
## | 27 | 2021-07-14 | Wednesday | 196.78 |
## +----+------------+-----------------+---------------------+
## | 28 | 2021-07-15 | Thursday | 201.37 |
## +----+------------+-----------------+---------------------+
## | 29 | 2021-07-16 | Friday | 204.84 |
## +----+------------+-----------------+---------------------+
## | 30 | 2021-07-17 | Saturday | 206.60 |
## +----+------------+-----------------+---------------------+
## | 31 | 2021-07-18 | Sunday | 207.16 |
## +----+------------+-----------------+---------------------+
## | 32 | 2021-07-19 | Monday | 206.94 |
## +----+------------+-----------------+---------------------+
## | 33 | 2021-07-20 | Tuesday | 205.65 |
## +----+------------+-----------------+---------------------+
## | 34 | 2021-07-21 | Wednesday | 203.14 |
## +----+------------+-----------------+---------------------+
## | 35 | 2021-07-22 | Thursday | 199.49 |
## +----+------------+-----------------+---------------------+
## | 36 | 2021-07-23 | Friday | 194.75 |
## +----+------------+-----------------+---------------------+
## | 37 | 2021-07-24 | Saturday | 188.58 |
## +----+------------+-----------------+---------------------+
## | 38 | 2021-07-25 | Sunday | 180.69 |
## +----+------------+-----------------+---------------------+
## | 39 | 2021-07-26 | Monday | 170.84 |
## +----+------------+-----------------+---------------------+
## | 40 | 2021-07-27 | Tuesday | 159.00 |
## +----+------------+-----------------+---------------------+
## | 41 | 2021-07-28 | Wednesday | 146.31 |
## +----+------------+-----------------+---------------------+
## | 42 | 2021-07-29 | Thursday | 135.11 |
## +----+------------+-----------------+---------------------+
## | 43 | 2021-07-30 | Friday | 127.19 |
## +----+------------+-----------------+---------------------+
plot(forecasting_NNAR,xlab = paste ("Time in", frequency ,y_lab , sep=" "), ylab=y_lab)
x1_test <- ts(testing_data, start =(rows-validation_data_days+1) )
lines(x1_test, col='red',lwd=2)
graph1<-autoplot(forecasting_NNAR,xlab = paste ("Time in", frequency ,y_lab , sep=" "), ylab=y_lab)
graph1
message("System finished Modelling and Forecasting by using BATS, TBATS, Holt's Linear Trend,ARIMA Model, and SIR Model ==>",y_lab, sep=" ")
## System finished Modelling and Forecasting by using BATS, TBATS, Holt's Linear Trend,ARIMA Model, and SIR Model ==>Forecast Third wave infection cases in Chelyabinsk
message(" Thank you for using our System For Modelling and Forecasting ==> ",y_lab, sep=" ")
## Thank you for using our System For Modelling and Forecasting ==> Forecast Third wave infection cases in Chelyabinsk