ENGM 745 Forecasting for Business & Technology Paula Jensen South Dakota School of Mines and Technology, Rapid City 3rd Session 2/01/12: Chapter 3 Moving.

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ENGM 745 Forecasting for Business & Technology Paula Jensen South Dakota School of Mines and Technology, Rapid City 3rd Session 2/01/12: Chapter 3 Moving Averages and Exponential Smoothing

Agenda & New Assignment ch3(1,5,8,11) Business Forecasting 6 th Edition J. Holton Wilson & Barry Keating McGraw-Hill

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

Moving Averages 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

Alternative interpretation

Simple Exponential Smoothing Why they call it exponential property

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

Holt's Exponential Smoothing (Double Holt in ForecastX TM )

ForecastX TM Conventions for Smoothing Constants Alpha (  ) =the simple smoothing constant Gamma (  ) =the trend smoothing constant Beta (  ) =the seasonality smoothing constant

Holt's Exponential Smoothing 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 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 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 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 Cover Single Exponential, Holt’s, Winters, ADRES I will be sending an with a link to get onto the Harvard link for a case study. Take the quiz online to brush up on Statistic skills.