Consider the following gasoline sales time series. If needed, round your answers to two decimal digits. Week Sales (1,000s of gallons) 1 17 2 21 3 19 4 23 5 18 6 16 7 20 8 18 9 22 10 20 11 15 12 22 (a) Show the exponential smoothing forecasts using α = 0.1, and α = 0.2. ExponentialSmoothing Week α = 0.1 α = 0.2 13 (b) Applying the MSE measure of forecast accuracy, would you prefer a smoothing constant of α = 0.1 or α = 0.2 for the gasoline sales time series? An smoothing constant provides a more accurate forecast, with an overall MSE of . (c) Are the results the same if you apply MAE as the measure of accuracy? An smoothing constant provides a more accurate forecast, with an overall MAE of . (d) What are the results if MAPE is used? An smoothing constant provides a more accurate forecast, with an overall MAPE of .

Consider the following gasoline sales time series. If needed, round your answers to two decimal digits. Week Sales (1,000s of gallons) 1 17 2 21 3 19 4 23 5 18 6 16 7 20 8 18 9 22 10 20 11 15 12 22 (a) Show the exponential smoothing forecasts using α = 0.1, and α = 0.2. ExponentialSmoothing Week α = 0.1 α = 0.2 13 (b) Applying the MSE measure of forecast accuracy, would you prefer a smoothing constant of α = 0.1 or α = 0.2 for the gasoline sales time series? An smoothing constant provides a more accurate forecast, with an overall MSE of . (c) Are the results the same if you apply MAE as the measure of accuracy? An smoothing constant provides a more accurate forecast, with an overall MAE of . (d) What are the results if MAPE is used? An smoothing constant provides a more accurate forecast, with an overall MAPE of .

Essentials of Business Analytics (MindTap Course List)

2nd Edition

ISBN:9781305627734

Author:Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann, David R. Anderson

Publisher:Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann, David R. Anderson

Chapter8: Time Series Analysis And_forecasting

Section: Chapter Questions

Problem 8P

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