library("forecast", lib.loc="~/R/win-library/3.1");library("ggplot2", lib.loc="~/R/win-library/3.1");library(ggfortify) # ggplot of Time Series data . library("tseries", lib.loc="~/R/win-library/3.1")

## Loading required package: zoo

##

## Attaching package: 'zoo'

##

## The following objects are masked from 'package:base':

##

## as.Date, as.Date.numeric

##

## Loading required package: timeDate

## This is forecast 6.1

##

## Loading required package: grid

## Loading required package: scales

library("tseries", lib.loc="~/R/win-library/3.1")

#Additive Decomposition into Data [Yt], Seasonal [St] , Trend [Mt] and Remainder[Et]

# ?stl == forecast.stl {forecast} and stl {stats} # We use- stl {stats}

decomp<-stl(S,s.window = 12,t.window = 10) ;head(decomp$time.series) ;str(

summary(decomp) # Reading the IQR values from SUMMARY we see --

# We may note,Vertical Grey Bars - seen on right of each plot- signify relative importance Component.

# Longest Vertical bar is of - STL.remainder == 4.9% , which means its Least Significant or NOT Imortant acf(decomp$time.series[,3],main="Auto Correlation Function - ACF - Residuals")