Activities: Week 4

Australian brick production

We will use the Bricks data from aus_production (Australian quarterly clay brick production 1956–-2005).

• Use an STL decomposition to calculate the trend-cycle and seasonal indices. (Experiment with the seasonal window.)
• Compute and plot the seasonally adjusted data.
• Use a naïve method to produce forecasts of the seasonally adjusted data.
• Use decomposition_model() to reseasonalise the results, giving forecasts for the original data.
• Repeat with a robust STL decomposition. Does it make much difference?

Afghanistan population

The annual population of Afghanistan is available in the global_economy data set.

• Plot the data and comment on its features. Can you observe the effect of the Soviet-Afghan war?
• Fit a linear trend model and compare this to a piecewise linear trend model with knots at 1980 and 1989.
• Generate forecasts from these two models for the five years after the end of the data. Which looks better? Why?

Olympic running times

Data set olympic_running contains the winning times (in seconds) in each Olympic Games sprint, middle-distance and long-distance track events from 1896 to 2016.

• Plot the winning time against the year for each event. Describe the main features of the plot.
• Fit a regression line to the data for each event. Obviously the winning times have been decreasing, but at what average rate per year?
• Predict the winning time for each race in the 2028 Olympics. Do they look reasonable?
• Adjust your model to make the trends piecewise linear. Do the forecasts look more reasonable?