How can you handle above effect without adding another layer

Assignment Help Computer Engineering

Reference no: EM131925997

Problem

Australian Wine Sales. Figure 18.12 shows time plots of monthly sales of six types of Australian wines (red, rose, sweet white, dry white, sparkling, and fortified) for 1980-1994 (Data are available in AustralianWines.csv, source: Hyndman, R.J., Time Series Data Library, Accessed on 07/25/15.). The units are thousands of liters. You are hired to obtain short term forecasts (2-3 months ahead) for each of the six series, and this task will be repeated every month.

a. Which forecasting method would you choose if you had to choose the same method for all series? Why?

b. Fortified wine has the largest market share of the above six types of wine. You are asked to focus on fortified wine sales alone and produce as accurate as possible forecasts for the next 2 months.

• Start by partitioning the data using the period until December 1993 as the training set.

• Apply Holt-Winter's exponential smoothing to sales with an appropriate season length (use the default values for the smoothing constants).

c. Create an ACF plot for the residuals from the Holt-Winter's exponential smoothing until lag 12.

i. Examining this plot, which of the following statements are reasonable conclusions?

• Decembers (month 12) are not captured well by the model.

• There is a strong correlation between sales on the same calendar month.

1472_Figure 18.12.jpg

• The model does not capture the seasonality well.

• We should try to fit an autoregressive model with lag 12 to the residuals.

• We should first deseasonalize the data and then apply Holt-Winter's exponential smoothing.

ii. How can you handle the above effect without adding another layer to your model?

Reference no: EM131925997

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