Fill in the table by preparing forecasts based on a five-year moving average, a three-year moving average, and exponential smoothing (w = 0.9 and w = 0.3). (Note: The exponential smoothing forecasts may be begun by assuming Yt + 1 = Yt.) Year Actual Demand 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 1,400 1,435 1,460 1,475 1,480 1,475 1,460 1,510 1,550 1,580 Year 2005 2006 2007 2008 2009 RMSE The following table shows the square errors, Moving Average (5-year) 1,450 1,465 1,470 1,480 1,495 1,515 (5-year) 625 25 1,600 4,900 7,225 Moving Average Fill the table by calculating the root mean square error (RMSE) for each of the methods. (3-year) 9 289 1,444 4,624 5,329 (3-year) Square Error 1,432 ▼ 1,457 1,472 ▼ 1,477 ▼ 1,472 ▼ 1,482 ▼ 1,507 1,547 Three-year moving average O Exponential smoothing (w = 0.3) Five-year moving average O Exponential smoothing (w = 0.9) Exponential Smoothing (W = 0.9) (W = 0.3) 1,400 (Y-Y...), , for forecasts from 2005 through 2009. 1,400 Exponential Smoothing (W = 0.9) (W = 0.3) 16 484 225 0 2,304 2,500 2,025 5,625 1,156 6,724 Based on the RMSE criterion, which of the forecasting methods is the most accurate?
Fill in the table by preparing forecasts based on a five-year moving average, a three-year moving average, and exponential smoothing (w = 0.9 and w = 0.3). (Note: The exponential smoothing forecasts may be begun by assuming Yt + 1 = Yt.) Year Actual Demand 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 1,400 1,435 1,460 1,475 1,480 1,475 1,460 1,510 1,550 1,580 Year 2005 2006 2007 2008 2009 RMSE The following table shows the square errors, Moving Average (5-year) 1,450 1,465 1,470 1,480 1,495 1,515 (5-year) 625 25 1,600 4,900 7,225 Moving Average Fill the table by calculating the root mean square error (RMSE) for each of the methods. (3-year) 9 289 1,444 4,624 5,329 (3-year) Square Error 1,432 ▼ 1,457 1,472 ▼ 1,477 ▼ 1,472 ▼ 1,482 ▼ 1,507 1,547 Three-year moving average O Exponential smoothing (w = 0.3) Five-year moving average O Exponential smoothing (w = 0.9) Exponential Smoothing (W = 0.9) (W = 0.3) 1,400 (Y-Y...), , for forecasts from 2005 through 2009. 1,400 Exponential Smoothing (W = 0.9) (W = 0.3) 16 484 225 0 2,304 2,500 2,025 5,625 1,156 6,724 Based on the RMSE criterion, which of the forecasting methods is the most accurate?
MATLAB: An Introduction with Applications
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ISBN:9781119256830
Author:Amos Gilat
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Chapter1: Starting With Matlab
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Transcribed Image Text:Fill in the table by preparing forecasts based on a five-year moving average, a three-year moving average, and exponential smoothing (w = 0.9 and
¹ Ŷt + 1 = Yt.)
W = 0.3). (Note: The exponential smoothing forecasts may be begun by assuming
Year Actual Demand
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
1,400
1,435
1,460
1,475
1,480
1,475
1,460
1,510
1,550
1,580
Year
2005
2006
2007
2008
2009
*
RMSE
Moving Average
(5-year)
1,450
1,465
1,470
1,480
1,495
1,515
(5-year)
625
25
1,600
4,900
7,225
Moving Average
2
The following table shows the square errors, (Yt -Ỹt-1)², for forecasts from 2005 through 2009.
Fill the table by calculating the root mean square error (RMSE) for each of the methods.
(3-year)
Square Error
(3-year)
9
289
1,444
4,624
5,329
1,432
1,457
1,472
1,477
1,472
1,482
1,507
1,547
Three-year moving average
Exponential smoothing (w = 0.3)
Five-year moving average
Exponential Smoothing
(W = 0.9)
(W = 0.3)
Exponential smoothing (w = 0.9)
1,400
1,400
Exponential Smoothing
(W = 0.9)
(W = 0.3)
16
484
225
0
2,304
2,500
2,025
5,625
1,156
6,724
Based on the RMSE criterion, which of the forecasting methods is the most accurate?
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