TM 745 Forecasting for Business & Technology Paula Jensen

1. TM 745 Forecasting for Business & TechnologyPaula Jensen 9th Session 3/28/2012: Chapter 8 Combining Forecast Results South Dakota School of Mines and Technology, Rapid City

2. Agenda & New Assignment • Chapter 8 problem 6, • Chapter 8 Combining Forecast Results • Final Project • This week discussion questions listed in D2L

4. Combining Forecast Results • Intro • Bias • Ex. What can be combined? • How to get the weights?

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

6. 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

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

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

9. An Example: Combined fit • Combined Improvements • 1) Optimal weights yield considerable improvements

10. 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)

11. What Forecasts Are Combined?

12. 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

13. 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)

14. Minimize variances of forecasts

15. Adaptive weights based on each error

16. Optimal linear composite

17. 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?

18. Regression for Combining: Household Cleaner, application • Sales by Sales Force Composite • Sales by Winter’s Method (5th fig. dates new)

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

20. 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

21. Forecasting THS with a Combined Forecast

22. Comment from the field • Delfield Company Food Service Co.

23. Integrative Case: The Gap 4th

24. Integrative Case: The Gap 4th

25. Integrative Case: The Gap 4th