Transcript Moving Averages and Exponential Smoothing Holt

ENGM 745 Forecasting for
Business & Technology
Paula Jensen
3rd Session 2/01/12:
Chapter 3 Moving Averages and
Exponential Smoothing
South Dakota School of Mines and
Technology, Rapid City
Agenda & New Assignment
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ch3(1,5,8,11)
Business Forecasting 6th Edition
J. Holton Wilson & Barry Keating
McGraw-Hill
Moving Averages &
Exponential Smoothing
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All basic methods based on smoothing
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1. Moving averages
2. Simple exponential smoothing
3. Holt's exponential smoothing
4. Winters' exponential smoothing
5. Adaptive-response-rate single
exponential smoothing
Moving Averages
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Ex. “Three Quarter Moving Average”
(1999Q1+1999Q2+1999Q3)/3 =
Forecast for 1999Q4
Slutsky-Yule effect: Any moving
average could appear to be a
cycle, because it is a serially
correlated set of random
numbers.
Simple Exponential Smoothing
Simple Exponential Smoothing
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Alternative interpretation
Simple Exponential Smoothing
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Why they call it exponential property
Simple Exponential Smoothing
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Advantages
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Simpler than other forms
Requires limited data
Disdvantages
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Lags behind actual data
No trend or seasonality
Holt's Exponential Smoothing
(Double Holt in ForecastXTM)
ForecastXTM Conventions for
Smoothing Constants
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Alpha (a) =the simple smoothing
constant
Gamma (g) =the trend smoothing
constant
Beta (b) =the seasonality smoothing
constant
Holt's Exponential Smoothing
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ForecastX will pick the smoothing
constants to minimize RMSE
Some trend, but no seasonality
Call it linear trend smoothing
Winters'
Adaptive-Response-Rate
Single Exponential Smoothing
Adaptive-Response-Rate
Single Exponential Smoothing
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Adaptive is a clue to how it works
No direct way of handling seasonality
Does not handle trends
ForecastX has different algorithm
Using Single, Holt's, or ADRES
Smoothing to Forecast a
Seasonal Data Series
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1. Calculate seasonal indices for the
series. Done in HOLT WINTERS
ForecastX™.
2. Deseasonalize the original
data by dividing each value by
its corresponding seasonal index.
Using Single, Holt's, or ADRES
Smoothing to Forecast a
Seasonal Data Series
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3. Apply a forecasting method (such as ES,
Holt's, or ADRES) to the deseasonalized
series to produce an intermediate forecast of
the deseasonalized data.
4. Reseasonalize the series by
multiplying each deseasonalized
forecast by its corresponding seasonal
index.
Conclusion
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Cover Single Exponential, Holt’s,
Winters, ADRES
I will be sending an e-mail with a link to
get onto the Harvard link for a case
study.
Take the quiz online to brush up on
Statistic skills.