Consider the following gasoline time series data.Gasoline Sales Time SeriesWeek Sales (1000s of Gallons)117221319423518616720818922102011151222show the exponential smoothing forecasts using ax= 0.1.Applying the MSE measure of forecast accuracy, would you prefer a smoothing constant of = ax.1 or ax= .2 for the gasoline sales time series (to 2 decimals)?MSE for ax= .1 MSE for ax= .2 Are the results the same if you apply MAE as the measure of accuracy (to 2 decimals)?MAE for ax= .1 MAE for ax= .2 What are the results if MAPE is used (to 2 decimals)?MAPE for ax= .1 %MAPE for ax= .2 %

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Answered: Consider the following gasoline time…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Consider the following gasoline time series data.

Gasoline Sales Time Series

Week Sales (1000s 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

show the exponential smoothing forecasts using ax= 0.1.

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

MSE for ax= .1

MSE for ax= .2
Are the results the same if you apply MAE as the measure of accuracy (to 2 decimals)?

MAE for ax= .1

MAE for ax= .2
What are the results if MAPE is used (to 2 decimals)?

MAPE for ax= .1 %

MAPE for ax= .2 %

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