We will use the Bricks data from aus$\_$product ion $($Australian quarterly clay brick

production $1956-2005)$ for this exercise.

a$.$ Use an STL decomposition to calculate the trend$-$cycle and seasonal indices.

$($Experiment with having fixed or changing seasonality.$)$

b$.$ Compute and plot the seasonally adjusted data.

c$.$ Use a na$$ve method to produce forecasts of the seasonally adjusted data.

d$.$ Use decomposition$\_$model $()$ to reseasonalise the results, giving forecasts for the

original data.

e$.$ Do the residuals look uncorrelated?

f$.$ Repeat with a robust STL decomposition. Does it make much difference?

g$.$ Compare forecasts from decomposition$\_$model$()$ with those from SNAIVE$(),$ using a

test set comprising the last $2$ years of data. Which is better?