Choose one of the following seasonal time series: hsales , auscafe , qauselec , qcement , qgas . a. Do the data need transforming? If so, find a suitable transformation. b. Are the data stationary? If...

Choose one of the following seasonal time series: hsales , auscafe , qauselec , qcement , qgas .

a. Do the data need transforming? If so, find a suitable transformation.

b. Are the data stationary? If not, find an appropriate dierencing which yields stationary data.

c. Identify a couple of ARIMA models that might be useful in describing the time series. Which of your models is the best according to their AIC values?

d. Estimate the parameters of your best model and do diagnostic testing on the residuals. Do the residuals resemble white noise? If not, try to find another ARIMA model which ts better.

e. Forecast the next 24 months of data using your preferred model.

f. Compare the forecasts obtained using ets() .