# 2.8 Exercises

- 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.- Monthly total of people on unemployed benefits in Australia (January 1956–July 1992).
- Monthly total of accidental deaths in the United States (January 1973–December 1978).
- 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.

- Use the Dow Jones index (data set
`dowjones`

) to do the following:- Produce a time plot of the series.
- Produce forecasts using the drift method and plot them.
- Show that the graphed forecasts are identical to extending the line drawn between the first and last observations.
- Try some of the other benchmark functions to forecast the same data set. Which do you think is best? Why?

- Consider the daily closing IBM stock prices (data set
`ibmclose`

).- Produce some plots of the data in order to become familiar with it.
- Split the data into a training set of 300 observations and a test set of 69 observations.
- Try various benchmark methods to forecast the training set and compare the results on the test set. Which method did best?

- Consider the sales of new one-family houses in the USA, Jan 1973 –
Nov 1995 (data set
`hsales`

).- Produce some plots of the data in order to become familiar with it.
- Split the
`hsales`

data set into a training set and a test set, where the test set is the last two years of data. - Try various benchmark methods to forecast the training set and compare the results on the test set. Which method did best?

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