We will use the bricksq data (Australian quarterly clay brick production. 1956–1994) for this exercise. a. Use an STL decomposition to calculate the trend-cycle and seasonal indices. (Experiment with...

We will use the bricksq data (Australian quarterly clay brick production. 1956–1994) for this exercise.

a. Use an STL decomposition to calculate the trend-cycle and seasonal indices. (Experiment with having xed or changing seasonality.)

b. Compute and plot the seasonally adjusted data.

c. Use a naïve method to produce forecasts of the seasonally adjusted data.

d. Use stlf() 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 dierence?

g. Compare forecasts from stlf() with those from snaive() , using a test set comprising the last 2 years of data. Which is better?