Exponential Smoothing Demystified - Forecast Pro · Exponential Smoothing Demystified Presented by ... Holt-Winters Exponential Smoothing ... A useful time series method that is easy

Exponential Smoothing Demystified

Presented byEric StellwagenVice President & CofounderBusiness Forecast Systems, [email protected]

Business Forecast Systems, Inc.68 Leonard StreetBelmont, MA 02478 USA(617) 484-5050www.forecastpro.com

© 2010, Business Forecast Systems, Inc. www.forecastpro.com

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.


What We’ll Cover

Introductions

Overview

Building the Model

Interpreting the Model

Example

Summary

Q&A


Exponential Smoothing

Pros:

Simple to understand and explain.

Widely accepted and used.

Often most accurate.

Adaptive.

Robust.

Easy to apply.

Can be automated.


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.


Data Length Example


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.


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)


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


Example


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.


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


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.


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


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


Questions?


Thank you for attending!

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Exponential Smoothing Demystified - Forecast Pro · Exponential Smoothing Demystified Presented by ... Holt-Winters Exponential Smoothing ... A useful time series method that is easy