Consider the following gasoline time series data. Click on the webfile logo to r

Question

Consider the following gasoline time series data. Click on the webfile logo to reference the data.

Show the exponential smoothing forecasts using = .1 and = .2

a. Applying the MSE measure of forecast accuracy, would you prefer a smoothing constant of = .1 or = .2 for the gasoline sales time series? Calculate the MSE for each smoothing constant (to 2 decimals).

MSE for = .1 ______

MSE for = .2 ______

(Select your answer: alpha equal to .1 provides more accurate forecasts based upon MSE, alpha equal to .2 provides more accurate forecasts based upon MSE)

b. Are the results the same if you apply MAE as the measure of accuracy? Calculate the MAE for each smoothing constant (to 2 decimals).

MAE for = .1 ______

MAE for = .2 ______

(Select your answer: alpha equal to .1 provides more accurate forecasts based upon MAE, alpha equal to .2 provides more accurate forecasts based upon MAE)

c. What are the results if MAPE is used (to 2 decimals)?

MAPE for = .1 ______%

MAPE for = .2 ______%

(Select your answer: alpha equal to .1 provides more accurate forecasts based upon MAPE, alpha equal to .2 provides more accurate forecasts based upon MAPE)

GASOLINE SALES TIME SERIES Sales (1000s Week Of gallons) 17 21 19 23 18 16 20 18 10 20 11 15 12 0) 23 18 16 20 18 22 20 15 22 (1 ll 719386082052 s a 1212-12-2212 S_eg ee 123456789012

Explanation / Answer

for alpha=0.1

for alpha =0.2

week sales(A) forecast(F) (A-F)^2 |A-F| |A-F|/A 1 17 2 21 17.00 16.00 4.00 0.19 3 19 17.40 2.56 1.60 0.08 4 23 17.56 29.59 5.44 0.24 5 18 18.10 0.01 0.10 0.01 6 16 18.09 4.38 2.09 0.13 7 20 17.88 4.48 2.12 0.11 8 18 18.10 0.01 0.10 0.01 9 22 18.09 15.32 3.91 0.18 10 20 18.48 2.32 1.52 0.08 11 15 18.63 13.18 3.63 0.24 12 22 18.27 13.94 3.73 0.17 Total 101.78 28.25 1.42 mean 9.25 2.57 12.95 MSE MAE MAPE

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