## 3.6 Exercises

- 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`

- United States GDP from
Why is a Box-Cox transformation unhelpful for the

`expsmooth::cangas`

data?What Box-Cox transformation would you select for your retail data (from Exercise 3 in Section 2.10)?

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

`aus_production`

, Economy class passengers between Melbourne and Sydney from`ansett`

, and Victorian Electricity Demand from`vic_elec`

.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`

)

- Australian Population (
Use the Facebook stock price (data set

`gafa_stock`

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

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?