Applied_Statistics_-_Data_Visualisation_/Lab 9.R at main · mananabbasi/Applied_Statistics_-_Data_Visualisation_ · GitHub

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install.packages("TTR")
install.packages("forecast")
library(TTR)
library(forecast)

kings <- scan("http://robjhyndman.com/tsdldata/misc/kings.dat",skip=3)
kings
kingstimeseries <- ts(kings)
kingstimeseries
births <- scan("http://robjhyndman.com/tsdldata/data/nybirths.dat")
birthstimeseries <- ts(births, frequency=12, start=c(1946,1))
birthstimeseries

souvenir <- scan("http://robjhyndman.com/tsdldata/data/fancy.dat")
souvenirtimeseries <- ts(souvenir, frequency=12, start=c(1987,1))
souvenirtimeseries

plot.ts(kingstimeseries)

plot.ts(birthstimeseries)

plot.ts(souvenirtimeseries)

logsouvenirtimeseries <- log(souvenirtimeseries)
plot.ts(logsouvenirtimeseries)

library("TTR")

kingstimeseriesSMA3 <- SMA(kingstimeseries,n=3)
plot.ts(kingstimeseriesSMA3)

kingstimeseriesSMA8 <- SMA(kingstimeseries,n=8)
plot.ts(kingstimeseriesSMA8)
birthstimeseriescomponents <- decompose(birthstimeseries)

birthstimeseriescomponents$seasonal
plot(birthstimeseriescomponents)

birthstimeseriescomponents <- decompose(birthstimeseries)
birthstimeseriesseasonallyadjusted <- birthstimeseries - birthstimeseriescomponents$seasonal

plot(birthstimeseriesseasonallyadjusted)

rain <- scan("http://robjhyndman.com/tsdldata/hurst/precip1.dat",skip=1)
rainseries <- ts(rain,start=c(1813))
plot.ts(rainseries)


rainseriesforecasts <- HoltWinters(rainseries, beta=FALSE, gamma=FALSE)
rainseriesforecasts

rainseriesforecasts$fitted
plot(rainseriesforecasts)

rainseriesforecasts$SSE

HoltWinters(rainseries, beta=FALSE, gamma=FALSE, l.start=23.56)
library("forecast")

rainseriesforecasts2 <- forecast(rainseriesforecasts, h=8)
rainseriesforecasts2

plot(rainseriesforecasts2)

acf(rainseriesforecasts2$residuals, lag.max=20 , na.action = na.pass)

Box.test(rainseriesforecasts2$residuals, lag=20, type="Ljung-Box")
plot.ts(rainseriesforecasts2$residuals)


plotForecastErrors <- function(forecasterrors) {
  # make a histogram of the forecast errors
  mybinsize <- IQR(forecasterrors) / 4
  mysd <- sd(forecasterrors)
  mymin <- min(forecasterrors) - mysd * 5
  mymax <- max(forecasterrors) + mysd * 3

  # generate normally distributed data with mean 0 and standard deviation mysd
  mynorm <- rnorm(10000, mean = 0, sd = mysd)
  mymin2 <- min(mynorm)
  mymax2 <- max(mynorm)

  if (mymin2 < mymin) {
    mymin <- mymin2
  }
  if (mymax2 > mymax) {
    mymax <- mymax2
  }

  # create bins for the histogram
  mybins <- seq(mymin, mymax, mybinsize)

  # make a red histogram of the forecast errors, with the normally distributed data overlaid
  hist(forecasterrors, col = "red", freq = FALSE, breaks = mybins)
  # freq = FALSE ensures the area under the histogram = 1

  # generate histogram data for normally distributed data
  myhist <- hist(mynorm, plot = FALSE, breaks = mybins)

  # plot the normal curve as a blue line on top of the histogram of forecast errors
  points(myhist$mids, myhist$density, type = "l", col = "blue", lwd = 2)
}

# Removing any NA values from residuals
rainseriesforecasts2$residuals <- rainseriesforecasts2$residuals[!is.na(rainseriesforecasts2$residuals)]

# Plotting the forecast errors
plotForecastErrors(rainseriesforecasts2$residuals)


skirts <- scan("http://robjhyndman.com/tsdldata/roberts/skirts.dat",skip=5)
skirtsseries <- ts(skirts,start=c(1866))
plot.ts(skirtsseries)

skirtsseriesforecasts <- HoltWinters(skirtsseries, gamma=FALSE)
 skirtsseriesforecasts

 plot(skirtsseriesforecasts)

 HoltWinters(skirtsseries, gamma=FALSE, l.start=608, b.start=9)

 skirtsseriesforecasts2 <- forecast(skirtsseriesforecasts, h=19)
 plot(skirtsseriesforecasts2)

 acf(na.omit(skirtsseriesforecasts2$residuals), lag.max=20)
 Box.test(na.omit(skirtsseriesforecasts2$residuals), lag=20, type="Ljung-Box" ,
            na.action = na.pass)