TM 745 Forecasting for Business

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TM 745 Forecasting for Business TechnologyDr.
Frank Joseph Matejcik
3rd Session 2/11/08 Chapter 3 Moving Averages
and Exponential Smoothing

• South Dakota School of Mines and Technology,
  Rapid City

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Agenda New Assignment

• ch3(1,5,8,11) Tentative Schedule
• Chapter 3 WK (with odd diversions)
• Try to use ForecastX for Autocorrelation
• Business Forecasting 5th Edition J. Holton
  Wilson Barry KeatingMcGraw-Hill

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Tentative Schedule
Chapters Assigned 28-Jan 1 problems
1,4,8 e-mail, contact 4-Feb 2 problems 4,
8, 9 11-Feb 3 problems 1,5,8,11 18-Feb
Presidents Day 25-Feb 4 problems 6,10 3-Mar
5 problems 5,8 10-Mar Exam 1 Ch 1-4
Revised 17-Mar Break 24-Mar Easter 31-Mar
6 problems 4, 7
Chapters Assigned 7-Apr 7 3,4,5(series
A) 7B 21-Apr 8 Problem 6 28-Apr
9 05-May Final
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Web Resources

• Class Web site on the HPCnet system
• http//sdmines.sdsmt.edu/sdsmt/directory/courses/2
  008sp/tm745M021
• Streaming video http//its.sdsmt.edu/Distance/
• Answers will be online. Linked from
• The same class session that is on the DVD is on
  the stream in lower quality. http//www.flashget.c
  om/ will allow you to capture the stream more
  readily and review the lecture, anywhere you can
  get your computer to run.

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Moving Averages Exponential Smoothing

• All basic methods based on smoothing
• 1. Moving averages
• 2. Simple exponential smoothing
• 3. Holt's exponential smoothing
• 4. Winters' exponential smoothing
• 5. Adaptive-response-rate single exponential
  smoothing

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Moving Averages

• Ex. Three Quarter Moving Average(1999Q11999Q2
  1999Q3)/3 Forecast for 1999Q4
• Slutsky-Yule effect Any moving average could
  appear to be acycle, because it is a serially
  correlated set of random numbers.

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Simple Exponential Smoothing
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Simple Exponential Smoothing

• Alternative interpretation

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Simple Exponential Smoothing

• Why they call it exponential property

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Simple Exponential Smoothing

• Advantages
• Simpler than other forms
• Requires limited data
• Disdvantages
• Lags behind actual data
• No trend or seasonality

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Holt's Exponential Smoothing(Double Holt in
ForecastXTM)
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ForecastXTM Conventions forSmoothing Constants

• Alpha (a) the simple smoothing constant
• Gamma (g) the trend smoothing constant
• Beta (b) the seasonality smoothing constant

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Holt's Exponential Smoothing

• ForecastX will pick the smoothing constants to
  minimize RMSE
• Some trend, but no seasonality
• Call it linear trend smoothing

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Winters'
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Adaptive-Response-Rate Single Exponential
Smoothing
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Adaptive-Response-Rate Single Exponential
Smoothing

• Adaptive is a clue to how it works
• No direct way of handling seasonality
• Does not handle trends
• ForecastX has different algorithm

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Using Single, Holt's, or ADRES Smoothing to
Forecast a Seasonal Data Series

• 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.

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Using Single, Holt's, or ADRES Smoothing to
Forecast a Seasonal Data Series

• 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.

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New-Product Forecasting(growth curve fitting)
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Gompertz Curve
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Logistic Curve
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Bass Model (See Chapter 1,too)
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Event Modeling

• Event Indices Legend
• 0. No event present
• Free-standing inserts (FSIs)
• FSI/radio, television, print campaign
• Load (trade promotion)
• Deload (month after effect of load)
• Thematics (themed adg campaign)
• Instant redeemable coupon (IRC)

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Forecasting Jewelry Sales using Exponential
Smoothing
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Forecasting Jewelry Sales using Exponential
Smoothing
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Forecasting Houses Sold Sales using Exponential
Smoothing
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Forecasting Houses Sold Sales using Exponential
Smoothing
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Summary

• All basic methods based on smoothing
• 1. Moving averages
• 2. Simple exponential smoothing
• 3. Holt's exponential smoothing
• 4. Winters' exponential smoothing
• 5. Adaptive-response-rate single exponential
  smoothing
• Use of Deseasonalized Series
• techniques not clear winners

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Integrative Case The Gap
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Solutions toCase Questions 1
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Solutions toCase Questions 3
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Case Questions Solutions to Case Questions

• Skipped the details of this one in lecture, but
  worth a read.
• Holts beats Winters but not by much Lets
  try it live.

Using ForecastX to Make Exponential Smoothing
Forecasts