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The following table shows the square errors, Fill the table by calculating the root mean square error (RMSE) for each of the methods. Year 2005 2006 07 2008 2009 RMSE Moving Average (5-year) 625 25 1,600 4,900 7,225 Square Error (3-year) 9 289 1,444 4,624 5,329 (Y-Ý... -1)². for forecasts from 2005 through 2009 Three-year moving average Based on the RMSE criterion, which of the forecasting methods is the most accurate? Exponential smoothing (w = 0.3) Five-year moving average Exponential smoothing (w = 0.9) Exponential Smoothing (W = 0.9) (W = 0.3) 16 484 225 0 2,304 2,500 2,025 5,625 1,156 6,724

The following table shows the square errors, Fill the table by calculating the root mean square error (RMSE) for each of the methods. Year 2005 2006 07 2008 2009 RMSE Moving Average (5-year) 625 25 1,600 4,900 7,225 Square Error (3-year) 9 289 1,444 4,624 5,329 (Y-Ý... -1)². for forecasts from 2005 through 2009 Three-year moving average Based on the RMSE criterion, which of the forecasting methods is the most accurate? Exponential smoothing (w = 0.3) Five-year moving average Exponential smoothing (w = 0.9) Exponential Smoothing (W = 0.9) (W = 0.3) 16 484 225 0 2,304 2,500 2,025 5,625 1,156 6,724

Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)
Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)
6th Edition
ISBN:9781337111348
Author:Bruce Crauder, Benny Evans, Alan Noell
Publisher:Bruce Crauder, Benny Evans, Alan Noell
Chapter5: A Survey Of Other Common Functions
Section5.3: Modeling Data With Power Functions
Problem 6E: Urban Travel Times Population of cities and driving times are related, as shown in the accompanying...
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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 The following table shows the square errors, RMSE Moving Average (5-year) (5-year) 625 25 1,600 4,900 7,225 Moving Average Square Error 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) (Y-Ý..), Three-year moving average Exponential smoothing (w = 0.3) Five-year moving average Exponential smoothing (w = 0.9) Exponential Smoothing (W = 0.9) (W = 0.3) 1,400 1,400 t- for forecasts from 2005 through 2009. 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?
Transcribed Image Text: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 The following table shows the square errors, RMSE Moving Average (5-year) (5-year) 625 25 1,600 4,900 7,225 Moving Average Square Error 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) (Y-Ý..), Three-year moving average Exponential smoothing (w = 0.3) Five-year moving average Exponential smoothing (w = 0.9) Exponential Smoothing (W = 0.9) (W = 0.3) 1,400 1,400 t- for forecasts from 2005 through 2009. 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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The given square errors for the forecasted period of 2005 to 2009 for the considered forecast models are given as:

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