1. For the following series, find an appropriate Box-Cox transformation in order to stabilize the variance and make a graph of the transformed series.
usnetelec
usgdp
mcopper
enplanements
2. Apply the average method, seasonal naïve method and drift method to forecast the ausbeer, WWWusage and bricksq data. Graph the forecasts. (Set forecast horizon to 10.)
3. Use the Dow Jones index (data set dowjones) to do the following:
a.Produce a time plot of the series.
b.Produce forecasts using the drift method and plot them.
c.Show that the forecasts are identical to extending the line drawn between the first and last observations.
d.Try using some of the other benchmark functions to forecast the same data set. Which do you think is best? Why?
R Syntax
For simple forecasts:
autoplot(beer2) +
autolayer(meanf(beer2, h=11)$mean, series=”Mean”) +
autolayer(naive(beer2, h=11)$mean, series=”Naïve”) +
autolayer(snaive(beer2, h=11)$mean, series=”Seasonal naïve”)
For Box-Cox transformation:
lambda <- BoxCox.lambda(elec)
autoplot(BoxCox(elec,lambda
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