TM 745 Forecasting for Business & Technology Dr. Frank Joseph Matejcik

1. TM 745 Forecasting for Business & TechnologyDr. Frank Joseph Matejcik 9th Session 4/21/08: Chapter 8 Combining Forecast Results South Dakota School of Mines and Technology, Rapid City

2. 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 President’s 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 14-Apr Out of town No class 21-Apr 8 Problem 6 28-Apr 9 05-May Final

3. Web Resources • Class Web site on the HPCnet system • http://sdmines.sdsmt.edu/sdsmt/directory/courses/2008sp/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.com/ will allow you to capture the stream more readily and review the lecture, anywhere you can get your computer to run.

4. Agenda & New Assignment • Chapter 8 problem 6, Chapter 9 no problems • Final is in two weeks • Study guide is posted • Chapter 8 Combining Forecast Results • We will do the student opinion survey

5. Combining Forecast Results • Intro • Bias • Ex. What can be combined? • How to get the weights? • Three techniques • Delfield • About ForecastX & combining

6. Introduction • 83% of experts believe that combining forecasts will produce more accurate forecasts than originals Collopy & Armstrong (1992) • Bates & Granger (1969) 1st idea • Why the best forecast may not be • 1) Some variables may be missing • 2) Discarded forecast may use a type of relation ship ignored in the best forecast

7. Bias • Unbiased here not used strictly as in Statistics • Statistics term unbiased • 1) a strong property of a statistics • 2) excludes reasonable statistics • Forecasters believes may influence forecasts • Try to ignore preconceived ideas • Fresh employees may help

8. An Example • Output indexes of Gas, Electricity, & Water • Linear Fit

9. An Example: Exponential fit • Uses a transform to fit it

10. An Example: Combined fit • Combined Improvements • 1) Optimal weights yield considerable improvements • 2) combined forecasts statement 2)page 395 is not quite correct. It happens. See table 8-1 (Separate File)

11. What Forecasts Are Combined? • Actual Practice try very different models • 1) Extract different predictive factors • a) transforms • b) model format • 2) Models use different variables • Air Travel Forecast • 1) Judgmental (Expert survey) • 2) Extrapolation (time series) • 3) Segmentation (Customer survey) • 4) Econometric (Causal regression)

12. What Forecasts Are Combined?

13. Choosing Weights for Combined Forecasts • Armstrong likes equal weights (ex ante) • MAPE’s reduced 6.6% • Better if >2 forecasts • Bates & Granger weight more accurate more heavily • In general combined forecast have smaller errors (ex’s b&w) • Book suggests weight more accurate more heavily

14. 3 Techniques for Selecting Weights • 1) Minimize variances of forecasts • 2) Adaptive weights based on each error • 3) Use regression on the forecasts. (Optimal linear composite)

15. Minimize variances of forecasts

16. Adaptive weights based on each error

17. Optimal linear composite

18. Optimal linear composite procedure • Constant term is found and tested, if tested to be in the model don’t apply it. • Comment b1 + b2 = about 1, b1, b2 > 1 • Comment F(1) & F(2) will likely haveconstant terms. So?

19. Regression for Combining: Household Cleaner, application • Sales by Sales Force Composite • Sales by Winter’s Method

20. Regression for Combining: Household Cleaner, application • Run usual regression • Force constant to be zero • Improved

21. Forecasting THS with a Combined Forecast • Time Series Decomposition chapter 6 • Multiple regression chapter 5THS=106.31 – 10.78(MR) - 0.45(ICS) • THS = -2.54+0.06(THSRF)+0.97(THSDF) (-1.2) (1.53) (31.8) • THS = 0.03(THSRF) + 0.97(THSDF) • RMSE combined 3.54 • RMSE THSDF Winter’s 3.55

22. Forecasting THS with a Combined Forecast

23. Forecasting DCS with a Combined Forecast

24. Forecasting DCS with a Combined Forecast

25. Comment from the field • Delfield Company Food Service Co. • 4th edition table below

26. Integrative Case: The Gap 4th

27. Integrative Case: The Gap 4th

28. Integrative Case: The Gap 4th

29. Using ForecastXTM to Combine Forecasts • It is just the regression that was given, so just remember to check the no intercept box.

30. We will do the student opinion survey, now.