This exercise uses the cangas data (monthly Canadian gas production in billions of cubic metres, January 1960 – February 2005). a. Plot the data using autoplot() , ggsubseriesplot() and ggseasonplot()...

This exercise uses the cangas data (monthly Canadian gas production in billions of cubic metres, January 1960 – February 2005).

a. Plot the data using autoplot() , ggsubseriesplot() and ggseasonplot() to look at the eect of the changing seasonality over time. What do you think is causing it to change so much?

b. Do an STL decomposition of the data. You will need to choose s.window to allow for the changing shape of the seasonal component.

c. Compare the results with those obtained using SEATS and X11. How are they dierent?