3.6 Exercises

  1. Consider the GDP information in global_economy. Plot the GDP per capita for each country over time. Which country has the highest GDP per capita? How has this changed over time?

  2. For the following series, find an appropriate Box-Cox transformation in order to stabilise the variance.

    • United States GDP from global_economy
    • Slaughter of Victorian “Bulls, bullocks and steers” in aus_livestock
    • Gas production from aus_production
  3. Why is a Box-Cox transformation unhelpful for the canadian_gas data?

  4. What Box-Cox transformation would you select for your retail data (from Exercise 4 in Section 2.10)?

  5. For each of the following series, make a graph of the data. If transforming seems appropriate, do so and describe the effect.

    • United States GDP from global_economy
    • Slaughter of Victorian “Bulls, bullocks and steers” in aus_livestock
    • Victorian Electricity Demand from vic_elec.
    • Gas production from aus_production
  6. Produce forecasts for the following series using whichever of NAIVE(y), SNAIVE(y) or RW(y ~ drift()) is more appropriate in each case:

    • Australian Population (global_economy)
    • Bricks (aus_production)
    • NSW Lambs (aus_livestock)
  7. Use the Facebook stock price (data set gafa_stock) to do the following:

    1. Produce a time plot of the series.
    2. Produce forecasts using the drift method and plot them.
    3. Show that the forecasts are identical to extending the line drawn between the first and last observations.
    4. Try using some of the other benchmark functions to forecast the same data set. Which do you think is best? Why?
  8. Produce forecasts for all of the Victorian series in aus_livestock using SNAIVE(). Plot the resulting forecasts including the historical data. Is this a reasonable benchmark for these series?