ENGM 745 Forecasting for Business & Technology Paula Jensen

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

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

3. 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

4. Moving Averages • Ex. “Three Quarter Moving Average”(1999Q1+1999Q2+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.

7. Simple Exponential Smoothing

8. Simple Exponential Smoothing • Alternative interpretation

9. Simple Exponential Smoothing • Why they call it exponential property

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

12. Holt's Exponential Smoothing(Double Holt in ForecastXTM)

13. ForecastXTM Conventions forSmoothing Constants • Alpha (a) =the simple smoothing constant • Gamma (g) =the trend smoothing constant • Beta (b) =the seasonality smoothing constant

17. Holt's Exponential Smoothing • ForecastX will pick the smoothing constants to minimize RMSE • Some trend, but no seasonality • Call it linear trend smoothing

18. Winters'

20. Adaptive-Response-Rate Single Exponential Smoothing

23. 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

24. 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.

25. 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.

27. Conclusion • 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.