Forecasting Principles and Practices Load the library fpp2

library(fpp2)
autoplot(melsyd[,"Economy.Class"])+
  ggtitle("Economy class passengers:Melbourne-Syney")+
  xlab("Year")+
  ylab("Thousands")

Seasonal Plots

ggseasonplot(a10,year.labels = TRUE,year.lables.left=TRUE)+
  ylab("$ million")+
  ggtitle("Seasonal plot: antidiabetic drug sales")

You can display it by polar coordinates also

ggseasonplot(a10, polar=TRUE) +
ylab("$ million") +
ggtitle("Polar seasonal plot: antidiabetic drug sales")

Season Subseries Plot: It emphasies the seasonal patterns where the data for each season are collected together in separate mini time phases.

ggsubseriesplot(a10) +
ylab("$ million") +
ggtitle("Seasonal subseries plot: antidiabetic drug sales")

The horizontal lines are means for each month. This also indicates the change in seasonality over time.

Scatterplots are used to explore relationship between time series Figure shows two time series: half hourly electricity demand ( in Gigawatts) and temperature ( Gigawatts)

autoplot(elecdemand[,c("Demand","Temperature")], facets=TRUE) +
xlab("Year: 2014") + ylab("") +
ggtitle("Half-hourly electricity demand: Victoria, Australia")

We can see the relationship between demand and temperature as below

qplot(Temperature, Demand, data=as.data.frame(elecdemand)) +
ylab("Demand (GW)") + xlab("Temperature (Celsius)")

Scatterplot matrices Quarterly visitor numbers for five seasons.

autoplot(visnights[,1:5], facets=TRUE) +
ylab("Number of visitor nights each quarter (millions)")

To see the scatterplot matrix pairwise

GGally::ggpairs(as.data.frame(visnights[,1:5]))
## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2

Lag Plots for identifying seasonality. Quarterly beer production

beer2 <- window(ausbeer, start=1992)
gglagplot(beer2)

Colored graphs show quarter. There is a strong positive relation at lags 4 and 8, reflecting the strong seasonality in the data. In lag 2 and 6 negative relation is seen in the data.