Post on 13-May-2018
Exponential Smoothing Demystified
Presented byEric StellwagenVice President & CofounderBusiness Forecast Systems, Inc.estellwagen@forecastpro.com
Business Forecast Systems, Inc.68 Leonard StreetBelmont, MA 02478 USA(617) 484-5050www.forecastpro.com
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Eric Stellwagen
Vice President, CEO & Cofounder ofBusiness Forecast Systems, Inc.
Coauthor of Forecast Pro product line.
Over 25 years in forecasting.
Currently serving on the board of directors of theInternational Institute of Forecasters and on the practitioner advisory board of Foresight: The International Journal of Applied Forecasting.
© 2010, Business Forecast Systems, Inc. www.forecastpro.com
What We’ll Cover
Introductions
Overview
Building the Model
Interpreting the Model
Example
Summary
Q&A
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Exponential Smoothing
Pros:
Simple to understand and explain.
Widely accepted and used.
Often most accurate.
Adaptive.
Robust.
Easy to apply.
Can be automated.
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Exponential Smoothing
Cons:
Is a time series model
Does not capture response to noncalendar-based events.
Does not allow for the introduction of explanatory variables.
Implementations vary and some are poor.
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Data Length Example
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Main Characteristics of Exponential Smoothing
A family of models.
Models three data components—level, trend and seasonality.
Assumes that each component is changing in time.
Assumes that there is noise (random variation).
Uses weights to reflect the relative emphasis given to the
recent vs. the distant past.
Estimates final values of the components and uses them to
construct forecasts.
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Holt-Winters Exponential Smoothing
NonseasonalAdditive
SeasonalMultiplicative
Seasonal
Constant
Level
Linear
Trend
Damped
Trend(0.95)
Exponential
Trend (1.05)
(SIMPLE)
(WINTERS)(HOLT)
(NN) (NA) (NM)
(LA)(LN)
(DN) (DM)(DA)
(EN) (EA) (EM)
(LM)
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Choosing an Exponential Smoothing Model
Determining the type of trend and type of seasonality can be
accomplished using:
Domain Knowledge (i.e., the forecaster’s knowledge of the
data)
Visual Inspection of data
An automatic algorithm
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Example
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Interpreting the Smoothing Weights
Weights take on values between
0 and 1.
The larger the value, the more
emphasis on recent history.
A best practice is to optimize the
weights to minimize the fitted
error.
Setting the weights to standard
values or narrow ranges should
be avoided.
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Interpreting the Smoothing Weights
The final values reflect conditions at the end of the time series and are used to create the forecasts.
1st Forecast = ( + ) *
2nd Forecast =
Level Trend June index
( + 2 * ) *Level Trend
65870 779.6 1.03
65870 779.6 1.027July index
= 68,682
= 69,272
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Summary
A useful time series method that is easy to apply.
Models three data components—level, trend and
seasonality.
Is adaptive to change.
Forecasts reflect current conditions, therefore the
method is best used for short-term forecasting.
© 2010, Business Forecast Systems, Inc. www.forecastpro.com
Forecasting Seminars and Workshops
BFS offers forecasting seminars and product
training workshops.
Public, on-site, and remote-based (via WebEx)
classes are available.
Learn more at www.forecastpro.com
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Forecast Pro
Examples from today’s Webinar used Forecast
Pro.
To learn more about Forecast Pro:
Request a live WebEx demo for your team
(submit your request as a question right now)
Visit www.forecastpro.com
Call us at 617-484-5050
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Questions?
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Thank you for attending!