Q$1.($slide pp $29)$ Based on the examples in module $2$ slides, create plots of the following time series: dole, bricksq, lynx$,$ goog. For the last plot, modify the axis labels and title.

Q$2.($slide pp $43)$ The arrivals data set comprises quarterly international arrivals $($in thousands$)$ to Australia from Japan, New Zealand, UK and the US$.$ Use autoplot$()$ and ggseasonplot$()$ to compare the differences between the arrivals from these four countries. Identify any unusual observations.

Q$3.($slide pp $73)$ Use the following graphics functions: gglagplot, ggAcf, to explore the time series hsales, gasoline. Can you spot any seasonality, cyclicity and trend? What do you learn about the series?

From the slides of forecast package:

Q$4.$ Generate the $50$ step$-$ahead forecasts for goog and auscafe time series with the method of mean, naive, seasonal naive and drift methods. Plot the forecasts using autoplot function.

Q$5.$ For the quarterly Australian beer production data ausbeer,

a$.$ Compute seasonal na$$ve forecasts from $1992.$

b$.$ Test if the residuals are white noise.