Advanced Data Analytics Using Python_ With Machine Learning, Deep Learning and NLP Examples ( 2023)

Chapter 6
Time Series
model provides the upper limit and the lower limit of the prediction in
forecasting.
asm_weekwise<-read.csv("F:/souravda/New ASM Weekwise.
csv",header=TRUE)
asm_weekwise$Week <- NULL
library(MASS, lib.loc="F:/souravda/lib/")
library(tseries, lib.loc="F:/souravda/lib/")
library(forecast, lib.loc="F:/souravda/lib/")
asm_weekwise[is.na(asm_weekwise)] <- 0
asm_weekwise[asm_weekwise <= 0] <- mean(as.matrix(asm_weekwise))
weekjoyforecastvalues <- data.frame( "asm" = integer(), "value"
= integer(), stringsAsFactors=FALSE)
for(i in 2:ncol(asm_weekwise))
{
asmname<-names(asm_weekwise)[i]
temparimadata<-asm_weekwise[,i]
m <- mean(as.matrix(temparimadata))
#print(m)
s <- sd(temparimadata)
#print(s)
temparimadata <- (temparimadata - m)
temparimadata <- (temparimadata / s)
temparima<-auto.arima(temparimadata, stationary = FALSE,
seasonal = TRUE, allowdrift = TRUE, allowmean = FALSE, biasadj
= FALSE)
tempforecast<-forecast(temparima,h=12)
#tempforecast <- (tempforecast * s)
#print(tempforecast)
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