A$.$ Create a training set for total Turnover in the Australian Food retailing Industry $($dataset aus$\_$retail$)$ by withholding the last five years as a test set. You will need to compute total turnover for all states by use of summarize or similar.

B$.$ Fit all the appropriate benchmark methods to the training set and forecast the periods covered by the test set.

C$.$ Compute the accuracy of your forecasts. Which method does best?

D$.$ Do the residuals from the best method resemble white noise?

This problem is similar to the previous $2,$ except that there will be a seasonal component to the data. Do you detect seasonality in the residuals? Any other problems that you notice?

Items to submit

$$ Code from the above, and output from running that code, along with descriptive interpretation for all plots given.A$.$ Create a training set for total Turnover in the Australian Food retailing Industry $($dataset aus$\_$retail$)$ by withholding the last five years as a test set. You will need to compute total turnover for all states by use of summarize or similar.

This problem is similar to the previous $2,$ except that there will be a seasonal component to the data. Do you detect seasonality in the residuals? Any other problems that you notice?

Items to submit

$$ Code from the above, and output from running that code, along with descriptive interpretation for all plots given.