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)
library(pander)
##
## Attaching package: 'pander'
## The following object is masked from 'package:ascii':
##
## Pandoc
##Global vriable##
Full_original_data <- read.csv("data.csv") # path of your data ( time series data)
original_data<-Full_original_data$daily.infection
y_lab <- "Forecast First wave infection cases in Chelyabinsk" # input name of data
Actual_date_interval <- c("2020/03/12","2020/05/31")
Forecast_date_interval <- c("2020/06/01","2020/08/31")
validation_data_days <-4
frequency<-"day"
Number_Neural<-50 # 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.
## 0.00 1.00 10.00 34.51 67.00 150.00
# calculate standard deviation
data.frame(kurtosis=kurtosis(original_data)) # calculate Cofficient of kurtosis
## kurtosis
## 1 2.938683
data.frame(skewness=skewness(original_data)) # calculate Cofficient of skewness
## skewness
## 1 0.9287743
data.frame(Standard.deviation =sd(original_data))
## Standard.deviation
## 1 39.75743
#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(2,50)
## Call: nnetar(y = data_series, size = Number_Neural)
##
## Average of 20 networks, each of which is
## a 2-50-1 network with 201 weights
## options were - linear output units
##
## sigma^2 estimated as 44.8
# 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 4 days by using NNAR Model for ==> Forecast First 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 4 days in NNAR Model for ==> Forecast First wave infection cases in Chelyabinsk"
paste(MAPE_Mean_All,"%")
## [1] "40.022 % MAPE 4 days Forecast First 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 4 days in NNAR Model for ==> Forecast First 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 | 2020-05-28 | Thursday | 82.00 | 83.39 | 1.69 % |
## +---+------------+-------------------------+-------------+------------------+-----------------+
## | 2 | 2020-05-29 | Friday | 141.00 | 71.04 | 49.615 % |
## +---+------------+-------------------------+-------------+------------------+-----------------+
## | 3 | 2020-05-30 | Saturday | 150.00 | 70.72 | 52.856 % |
## +---+------------+-------------------------+-------------+------------------+-----------------+
## | 4 | 2020-05-31 | Sunday | 136.00 | 59.94 | 55.926 % |
## +---+------------+-------------------------+-------------+------------------+-----------------+
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 | 2020-06-01 | Monday | 53.98 |
## +----+------------+-----------------+---------------------+
## | 2 | 2020-06-02 | Tuesday | 58.81 |
## +----+------------+-----------------+---------------------+
## | 3 | 2020-06-03 | Wednesday | 43.51 |
## +----+------------+-----------------+---------------------+
## | 4 | 2020-06-04 | Thursday | 23.12 |
## +----+------------+-----------------+---------------------+
## | 5 | 2020-06-05 | Friday | 43.75 |
## +----+------------+-----------------+---------------------+
## | 6 | 2020-06-06 | Saturday | 82.52 |
## +----+------------+-----------------+---------------------+
## | 7 | 2020-06-07 | Sunday | 46.77 |
## +----+------------+-----------------+---------------------+
## | 8 | 2020-06-08 | Monday | 37.45 |
## +----+------------+-----------------+---------------------+
## | 9 | 2020-06-09 | Tuesday | 87.51 |
## +----+------------+-----------------+---------------------+
## | 10 | 2020-06-10 | Wednesday | 68.31 |
## +----+------------+-----------------+---------------------+
## | 11 | 2020-06-11 | Thursday | 72.63 |
## +----+------------+-----------------+---------------------+
## | 12 | 2020-06-12 | Friday | 61.96 |
## +----+------------+-----------------+---------------------+
## | 13 | 2020-06-13 | Saturday | 52.37 |
## +----+------------+-----------------+---------------------+
## | 14 | 2020-06-14 | Sunday | 64.06 |
## +----+------------+-----------------+---------------------+
## | 15 | 2020-06-15 | Monday | 47.80 |
## +----+------------+-----------------+---------------------+
## | 16 | 2020-06-16 | Tuesday | 53.42 |
## +----+------------+-----------------+---------------------+
## | 17 | 2020-06-17 | Wednesday | 121.41 |
## +----+------------+-----------------+---------------------+
## | 18 | 2020-06-18 | Thursday | 136.16 |
## +----+------------+-----------------+---------------------+
## | 19 | 2020-06-19 | Friday | 76.14 |
## +----+------------+-----------------+---------------------+
## | 20 | 2020-06-20 | Saturday | 145.50 |
## +----+------------+-----------------+---------------------+
## | 21 | 2020-06-21 | Sunday | 160.29 |
## +----+------------+-----------------+---------------------+
## | 22 | 2020-06-22 | Monday | 89.46 |
## +----+------------+-----------------+---------------------+
## | 23 | 2020-06-23 | Tuesday | 163.85 |
## +----+------------+-----------------+---------------------+
## | 24 | 2020-06-24 | Wednesday | 158.38 |
## +----+------------+-----------------+---------------------+
## | 25 | 2020-06-25 | Thursday | 115.77 |
## +----+------------+-----------------+---------------------+
## | 26 | 2020-06-26 | Friday | 146.30 |
## +----+------------+-----------------+---------------------+
## | 27 | 2020-06-27 | Saturday | 63.95 |
## +----+------------+-----------------+---------------------+
## | 28 | 2020-06-28 | Sunday | 109.78 |
## +----+------------+-----------------+---------------------+
## | 29 | 2020-06-29 | Monday | 106.96 |
## +----+------------+-----------------+---------------------+
## | 30 | 2020-06-30 | Tuesday | 98.39 |
## +----+------------+-----------------+---------------------+
## | 31 | 2020-07-01 | Wednesday | 100.85 |
## +----+------------+-----------------+---------------------+
## | 32 | 2020-07-02 | Thursday | 87.78 |
## +----+------------+-----------------+---------------------+
## | 33 | 2020-07-03 | Friday | 99.55 |
## +----+------------+-----------------+---------------------+
## | 34 | 2020-07-04 | Saturday | 72.43 |
## +----+------------+-----------------+---------------------+
## | 35 | 2020-07-05 | Sunday | 94.44 |
## +----+------------+-----------------+---------------------+
## | 36 | 2020-07-06 | Monday | 68.71 |
## +----+------------+-----------------+---------------------+
## | 37 | 2020-07-07 | Tuesday | 83.14 |
## +----+------------+-----------------+---------------------+
## | 38 | 2020-07-08 | Wednesday | 72.12 |
## +----+------------+-----------------+---------------------+
## | 39 | 2020-07-09 | Thursday | 71.63 |
## +----+------------+-----------------+---------------------+
## | 40 | 2020-07-10 | Friday | 60.95 |
## +----+------------+-----------------+---------------------+
## | 41 | 2020-07-11 | Saturday | 52.95 |
## +----+------------+-----------------+---------------------+
## | 42 | 2020-07-12 | Sunday | 61.81 |
## +----+------------+-----------------+---------------------+
## | 43 | 2020-07-13 | Monday | 45.29 |
## +----+------------+-----------------+---------------------+
## | 44 | 2020-07-14 | Tuesday | 35.07 |
## +----+------------+-----------------+---------------------+
## | 45 | 2020-07-15 | Wednesday | 68.72 |
## +----+------------+-----------------+---------------------+
## | 46 | 2020-07-16 | Thursday | 78.89 |
## +----+------------+-----------------+---------------------+
## | 47 | 2020-07-17 | Friday | 69.18 |
## +----+------------+-----------------+---------------------+
## | 48 | 2020-07-18 | Saturday | 61.84 |
## +----+------------+-----------------+---------------------+
## | 49 | 2020-07-19 | Sunday | 54.65 |
## +----+------------+-----------------+---------------------+
## | 50 | 2020-07-20 | Monday | 62.27 |
## +----+------------+-----------------+---------------------+
## | 51 | 2020-07-21 | Tuesday | 44.86 |
## +----+------------+-----------------+---------------------+
## | 52 | 2020-07-22 | Wednesday | 33.58 |
## +----+------------+-----------------+---------------------+
## | 53 | 2020-07-23 | Thursday | 63.07 |
## +----+------------+-----------------+---------------------+
## | 54 | 2020-07-24 | Friday | 74.70 |
## +----+------------+-----------------+---------------------+
## | 55 | 2020-07-25 | Saturday | 66.10 |
## +----+------------+-----------------+---------------------+
## | 56 | 2020-07-26 | Sunday | 54.92 |
## +----+------------+-----------------+---------------------+
## | 57 | 2020-07-27 | Monday | 65.04 |
## +----+------------+-----------------+---------------------+
## | 58 | 2020-07-28 | Tuesday | 48.57 |
## +----+------------+-----------------+---------------------+
## | 59 | 2020-07-29 | Wednesday | 58.04 |
## +----+------------+-----------------+---------------------+
## | 60 | 2020-07-30 | Thursday | 91.31 |
## +----+------------+-----------------+---------------------+
## | 61 | 2020-07-31 | Friday | 79.05 |
## +----+------------+-----------------+---------------------+
## | 62 | 2020-08-01 | Saturday | 88.13 |
## +----+------------+-----------------+---------------------+
## | 63 | 2020-08-02 | Sunday | 70.69 |
## +----+------------+-----------------+---------------------+
## | 64 | 2020-08-03 | Monday | 77.12 |
## +----+------------+-----------------+---------------------+
## | 65 | 2020-08-04 | Tuesday | 66.74 |
## +----+------------+-----------------+---------------------+
## | 66 | 2020-08-05 | Wednesday | 56.61 |
## +----+------------+-----------------+---------------------+
## | 67 | 2020-08-06 | Thursday | 61.30 |
## +----+------------+-----------------+---------------------+
## | 68 | 2020-08-07 | Friday | 46.48 |
## +----+------------+-----------------+---------------------+
## | 69 | 2020-08-08 | Saturday | 40.75 |
## +----+------------+-----------------+---------------------+
## | 70 | 2020-08-09 | Sunday | 126.77 |
## +----+------------+-----------------+---------------------+
## | 71 | 2020-08-10 | Monday | 137.02 |
## +----+------------+-----------------+---------------------+
## | 72 | 2020-08-11 | Tuesday | 86.65 |
## +----+------------+-----------------+---------------------+
## | 73 | 2020-08-12 | Wednesday | 146.04 |
## +----+------------+-----------------+---------------------+
## | 74 | 2020-08-13 | Thursday | 138.74 |
## +----+------------+-----------------+---------------------+
## | 75 | 2020-08-14 | Friday | 115.61 |
## +----+------------+-----------------+---------------------+
## | 76 | 2020-08-15 | Saturday | 127.79 |
## +----+------------+-----------------+---------------------+
## | 77 | 2020-08-16 | Sunday | 79.11 |
## +----+------------+-----------------+---------------------+
## | 78 | 2020-08-17 | Monday | 136.01 |
## +----+------------+-----------------+---------------------+
## | 79 | 2020-08-18 | Tuesday | 134.13 |
## +----+------------+-----------------+---------------------+
## | 80 | 2020-08-19 | Wednesday | 107.94 |
## +----+------------+-----------------+---------------------+
## | 81 | 2020-08-20 | Thursday | 129.42 |
## +----+------------+-----------------+---------------------+
## | 82 | 2020-08-21 | Friday | 59.83 |
## +----+------------+-----------------+---------------------+
## | 83 | 2020-08-22 | Saturday | 87.16 |
## +----+------------+-----------------+---------------------+
## | 84 | 2020-08-23 | Sunday | 79.71 |
## +----+------------+-----------------+---------------------+
## | 85 | 2020-08-24 | Monday | 81.74 |
## +----+------------+-----------------+---------------------+
## | 86 | 2020-08-25 | Tuesday | 71.84 |
## +----+------------+-----------------+---------------------+
## | 87 | 2020-08-26 | Wednesday | 69.20 |
## +----+------------+-----------------+---------------------+
## | 88 | 2020-08-27 | Thursday | 58.35 |
## +----+------------+-----------------+---------------------+
## | 89 | 2020-08-28 | Friday | 56.77 |
## +----+------------+-----------------+---------------------+
## | 90 | 2020-08-29 | Saturday | 51.92 |
## +----+------------+-----------------+---------------------+
## | 91 | 2020-08-30 | Sunday | 46.87 |
## +----+------------+-----------------+---------------------+
## | 92 | 2020-08-31 | Monday | 60.86 |
## +----+------------+-----------------+---------------------+
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 First 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 First wave infection cases in Chelyabinsk