# 2.8 Exercises

1. For each of the following series (from the fma package), make a graph of the data. If transforming seems appropriate, do so and describe the effect.
1. Monthly total of people on unemployed benefits in Australia (January 1956–July 1992).
2. Monthly total of accidental deaths in the United States (January 1973–December 1978).
3. Quarterly production of bricks (in millions of units) at Portland, Australia (March 1956–September 1994).
Hints:
• data(package="fma") will give a list of the available data.
• To plot a transformed data set, use plot(BoxCox(x,0.5)) where x is the name of the data set and 0.5 is the Box-Cox parameter.
2. Use the Dow Jones index (data set dowjones) 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 graphed forecasts are identical to extending the line drawn between the first and last observations.
4. Try some of the other benchmark functions to forecast the same data set. Which do you think is best? Why?
3. Consider the daily closing IBM stock prices (data set ibmclose).
1. Produce some plots of the data in order to become familiar with it.
2. Split the data into a training set of 300 observations and a test set of 69 observations.
3. Try various benchmark methods to forecast the training set and compare the results on the test set. Which method did best?
4. Consider the sales of new one-family houses in the USA, Jan 1973 – Nov 1995 (data set hsales).
1. Produce some plots of the data in order to become familiar with it.
2. Split the hsales data set into a training set and a test set, where the test set is the last two years of data.
3. Try various benchmark methods to forecast the training set and compare the results on the test set. Which method did best?