1: Forecasting Shampoo Sales: The file ShampooSales.xls contains data on the monthly sales of a certain shampoo over a 3-year period.

# this code basically installed packages if not installed already and load the mentioned packages 
if (!require("pacman")) install.packages("pacman")
## Loading required package: pacman
pacman::p_load("moments","extRemes","stringi", "ggplot2", "TTR", "forecast","zoo","xts")
setwd("d:/google drive/FA/RCode")
op <- par(oma=c(5,7,1,1))
ShampooSales.data <- read.csv("ShampooSales.csv")
ShampooSales.ts <- ts(ShampooSales.data$Shampoo.Sales, start = c(1995,1), end = c(1997, 12), freq = 12)
ShampooSales.lm <- tslm(ShampooSales.ts ~ trend + I(trend^2)) 
par(op)

1A: Create a well-formatted time plot of the data.

Above graph clearly showed that this is a Upward Exponantial Trend with additive Seasonality.

1:B Which of the four components (level, trend, seasonality, noise) seem to be present in this series?

All 4 levels presnts in this time series

1:C Do you expect to see seasonality in sales of shampoo? Why?

Yes, we expects seasonality in sales of shampoo. After decomposing time series

##             Jan        Feb        Mar        Apr        May        Jun
## 1995 -19.193924  -2.218924 -48.175174  27.591493 -44.800174   6.345660
## 1996 -19.193924  -2.218924 -48.175174  27.591493 -44.800174   6.345660
## 1997 -19.193924  -2.218924 -48.175174  27.591493 -44.800174   6.345660
##             Jul        Aug        Sep        Oct        Nov        Dec
## 1995   2.951910  30.431076  -1.171007  20.295660  37.274826  -9.331424
## 1996   2.951910  30.431076  -1.171007  20.295660  37.274826  -9.331424
## 1997   2.951910  30.431076  -1.171007  20.295660  37.274826  -9.331424

There are 3 reasons of seasonality 1. People buy more shampoo during summer time (like jun, july and august) as shown in above table
2. People tends to buy more during Off season sales periods like Oct and/or November 3. April shows positive numbers which indicates people stocked up shampoo as they deffered their purchases during winter season.