9. With the gasoline time series data from Table 6.1, show the exponential smoothing fore- SELF test casts using a = 0.1. a. Applying the MSE measure of forecast accuracy, would you prefer a smoothing constant of a = 0.1 or a = 0.2 for the gasoline sales time series? b. Are the results the same if you apply MAE as the measure of accuracy? C. What are the results if MAPE is used?198 Chapter & Time Series Analysis and Forecasting TABLE 4.1 GASOLINE SALES TIME SERIES Sales (1000s of gallons) 17 21 19 WEB fileFIGURE 6 1 GASOLINE SALES TIME SERIES PLOT 20 Saba (totals of galkinsi 10 7 10 01 12 Week evidence to conclude that the time series is stationary, More advanced texts on forecasting discuss procedures for determining if a time series is stationary and provide methods for transforming a time series that is nonstationary into a stationary series. Changes in business conditions often result in a time series with a horizontal pattern that shifts to a new level at some point in time. For instance, suppose the gasoline distribu- tor signs a contract with the Vermont Sate Police to provide gasoline for state police cars located in southern Vermont beginning in week 13. With this new contract, the distributor naturally expects to see a substantial increase in weekly sales starting in week 13. Table 6.2 shows the number of gallons of gasoline sold for the original time series and the 10 weeks