Number 1

library(fpp2)
## Warning: package 'fpp2' was built under R version 3.4.4
## Loading required package: ggplot2
## Warning: package 'ggplot2' was built under R version 3.4.3
## Loading required package: forecast
## Warning: package 'forecast' was built under R version 3.4.4
## Loading required package: fma
## Warning: package 'fma' was built under R version 3.4.4
## Loading required package: expsmooth
## Warning: package 'expsmooth' was built under R version 3.4.4
library(readxl)
## Warning: package 'readxl' was built under R version 3.4.3
data <- read_xlsx("C:/Users/buffe/Documents/Forecasting Principles/Retail Data.xlsx", skip = 1)


ts1 <- ts(data[,3], start = c(1982, 4), deltat = 1/12)
autoplot(ts1)

library(seasonal)
## Warning: package 'seasonal' was built under R version 3.4.4
fit <- seas(ts1, x11="")
autoplot(fit)

Number 2

data <- cangas

autoplot(data)

ggsubseriesplot(data)

ggseasonplot(data)

stl.data <- stl(data, s.window = "periodic", robust=TRUE)

x11.data <- seas(data, x11 = "")

seats.data <- seas(data)

autoplot(stl.data)

autoplot(x11.data)

autoplot(seats.data)

I think that the seaaonality is changing do to supply and demand in the market. The SEATS and X11 differ by the remainder graph. The X11 has more data points that deiviate further away from one.

Number 3

data <- oil

ma.data <- ma(data,6)

autoplot(data, series = "Data") +
  autolayer(ma.data, series = "5-MA") +
  xlab("Year") + ylab("GWh") +
  scale_colour_manual(values=c("Data"="grey50", "5-MA"="red"),
                      breaks=c("Data","5-MA"))
## Warning: Removed 6 rows containing missing values (geom_path).

Number 4

data <- plastics

autoplot(plastics)

de.data <- decompose(plastics, type = "multiplicative")

autoplot(de.data)

autoplot(seasadj(de.data))

data.out.mid <- plastics

data.out.mid[25] <- data.out.mid[25] + 500

autoplot(seasadj(decompose(data.out.mid, type = "multiplicative")))

data.out.end <- plastics

data.out.end[55] <- data.out.end[55] + 500

autoplot(seasadj(decompose(data.out.end, type = "multiplicative")))

Number 5

data <- fancy
autoplot(data)

fit <- stlf(fancy, method = "rwdrift", lambda = BoxCox.lambda(fancy))
autoplot(fit)

data2 <- writing
autoplot(data2)

fit2 <- stlf(data2, method = "naive")
autoplot(fit2)