(b) 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? (Round your answers to two decimal places.) Using a - 0.1 MSE is 12.16 , and using a- 0.2 MSE is 11.66 , so we would prefer a smoothing constant of 0.2 v (c) Are the results the same if you apply MAE as the measure of accuracy? (Round your answers to two decimal places.) Using a = 0.1 MAE is 3.02 ,and using a - 0.2 MAE is 3.06 , so the results are not the same as applying MSE as our measure of accuracy. (d) What are the results if MAPE is used? (Round your answers to two decimal places.) Using a - 0.1 MAPE is 21.09 X %, and using a = 0.2 MAPE is 15.36 X %, so we would prefer a smoothing constant of 0.1
(b) 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? (Round your answers to two decimal places.) Using a - 0.1 MSE is 12.16 , and using a- 0.2 MSE is 11.66 , so we would prefer a smoothing constant of 0.2 v (c) Are the results the same if you apply MAE as the measure of accuracy? (Round your answers to two decimal places.) Using a = 0.1 MAE is 3.02 ,and using a - 0.2 MAE is 3.06 , so the results are not the same as applying MSE as our measure of accuracy. (d) What are the results if MAPE is used? (Round your answers to two decimal places.) Using a - 0.1 MAPE is 21.09 X %, and using a = 0.2 MAPE is 15.36 X %, so we would prefer a smoothing constant of 0.1
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
Please answer part D
![(b) 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? (Round your answers
to two decimal places.)
Using a - 0.1 MSE Is 12.16
, and using a- 0.2 MSE is 11.66
, so we would prefer a smoothing constant of 0.2 v
(c) Are the results the same if you apply MAE as the measure of accuracy? (Round your answers to two decimal places.)
Using a - 0.1 MAE is 3.02
,and using a = 0.2 MAE is 3.06
, so the results are not
the same as applying MSE as our measure of
accuracy.
(d) What are the results if MAPE is used? (Round your answers to two decimal places.)
Using a - 0.1 MAPE is 21.09
X %, and using a = 0.2 MAPE is 15.36
X %, so we would prefer a smoothing constant of 0.1 v](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F55d7d476-142a-464a-9a3c-33461276aa85%2F9a317433-cbe1-4e79-ad91-8086df48d181%2F2p3zcf7_processed.jpeg&w=3840&q=75)
Transcribed Image Text:(b) 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? (Round your answers
to two decimal places.)
Using a - 0.1 MSE Is 12.16
, and using a- 0.2 MSE is 11.66
, so we would prefer a smoothing constant of 0.2 v
(c) Are the results the same if you apply MAE as the measure of accuracy? (Round your answers to two decimal places.)
Using a - 0.1 MAE is 3.02
,and using a = 0.2 MAE is 3.06
, so the results are not
the same as applying MSE as our measure of
accuracy.
(d) What are the results if MAPE is used? (Round your answers to two decimal places.)
Using a - 0.1 MAPE is 21.09
X %, and using a = 0.2 MAPE is 15.36
X %, so we would prefer a smoothing constant of 0.1 v
![(a) Show the exponential smoothing forecasts using a = 0.1. (Round your answers to three decimal places.)
Time Series
Week
Value
Forecast
1
17
21
17
3
20
17.4
4
24
17.66
18
18.294
6
17
18.2646
20
18.13814
8
17
18.324
23
18.191
10
21
18.672
11
15
18.905
12
23
18.514](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F55d7d476-142a-464a-9a3c-33461276aa85%2F9a317433-cbe1-4e79-ad91-8086df48d181%2F255s1k_processed.jpeg&w=3840&q=75)
Transcribed Image Text:(a) Show the exponential smoothing forecasts using a = 0.1. (Round your answers to three decimal places.)
Time Series
Week
Value
Forecast
1
17
21
17
3
20
17.4
4
24
17.66
18
18.294
6
17
18.2646
20
18.13814
8
17
18.324
23
18.191
10
21
18.672
11
15
18.905
12
23
18.514
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