« The ABC of Automated Forecast Model Profile Selection » The work presents a step-by-step procedure for automated selection of appropriate (useful & credible) forecasting models, based on the  observable profiles  drawn from historic data. Analyzing core features of past demand, decision metrics are assessed, and then applied as gateways through a decision tree. An approach for filtering trend and seasonality from noise is introduced, which is designed as outlier-resistant. The practical implementation of the procedure, steps and metrics are also presented, aiming at its continuous operation, embedded into a forecast decision support system (FDSS).

1. The ABC of Automated
Forecast Model Profile
Selection
ISIS SANTOS COSTA
HANS LEVENBACH

2. Sample Data

3. The ABC of Automated Forecast Model Profile Selection
Goal
To enable automatic selection of Forecasting Models
 Forecast Decision Support System
Procedure
1. Data Prep
2. ANOVA
3. Sources of variability
4. % ETS
5. Seasonal Strength & Trend Strength Factors
6. Seasonal Influence & Trend Influence
7. Seasonal Influence & Trend Influence Factors  Empirical diagram

4. 1987 1988 1989 1990 1991 1992 1993 1994
Jan 786152 839670 974188 903905 885370 959456 1063319 1001666
Feb 887466 886806 955943 974746 971191 1059044 1016060 1073196
Mar 1179318 1220361 1269982 1317009 1282507 1321280 1417406 1421423
M Apr 1279396 1376184 1455327 1414099 1440099 1468333 1556240 1577321
O May 1317952 1408277 1463003 1484942 1457659 1561624 1608882 1600991
N Jun 1277096 1361691 1411940 1421063 1456850 1425970 1524063 1594481
T Jul 1239127 1262819 1342884 1342884 1362356 1142066 1503225 1510052
H Aug 1170964 1267118 1301696 1301696 1309305 1444047 1448443 1436164
Sep 1177436 1225871 1261578 1261578 1302742 1380209 1416191 1404978
Oct 1349938 1405643 1388512 1388512 1466332 1473327 1593538 1585409
Nov 1054226 1125935 1113859 1113859 1181893 1146961 1274462 1234848
Dec 813315 863217 869067 869067 854615 920010 969445 923115
YEAR
Source: Hans Levenbach, CHANGE & CHANCE EMBRACED
Step 1: ANOVA

5. Step 2: ANOVA (Excel Add-In)

6. Step 2: ANOVA: Data Analysis

7. Step 2: ANOVA: two-way without replication

8. Step 3: Sum of squares, sources of variability

9. Step 4: % ETS
86% Seasonality
12% Trend
3% Other
Sources of variability

10. Step 4: % ETS (seasonal chart)

11. Step 4: % ETS (decomposed data: Level)

12. Step 4: % ETS (decomposed data: Trend)

13. Step 4: % ETS (decomposed data: Seasonality)

14. Step 5: Seasonal Strength & Trend Strength Factors
Define Seasonal Strength Factor SFSeas and Trend Strength Factor SFTrnd by
SFSeas = SS(Seasonality)/[SS(Seasonality) + SS(Other)]
SFTrnd = SS(Trend)/[ SS(Trend)+SS(Other)]
Strength Factors range 0 < SF <1.

15. SFSeas 0,968924
SFTrnd 0,808759
Strength Factors
Step 5: Seasonal Strength & Trend Strength Factors

16. Step 6: Seasonal Influence & Trend Influence
Define Seasonal Influence ISeas and Trend Influencec ITrnd by
ISeas = MS(Seasonality)/MS(Other)
ITrnd = MS(Trend)/MS(Other)
These are the F statistics in the ANOVA table
If ISeas < 3 or so, no seasonal model
If ITrnd < 3 or so, no trend
If both < 3 or so, we should select ETS (*, N, N) model

17. ISeas 218,2524
ITrnd 46,51895
Influence
Step 6: Seasonal Influence & Trend Influence

18. Step 7: Seasonal Influence & Trend Influence Factors
Transfom the Influences ISeas, ITrnd into Influence Factors
IFSeas = ISeas / (1 + ISeas)
IFTrnd = ITrnd / (1 + ITrnd)
Then empirically determine the cutoffs
High IFSeas and high IFTrnd would suggest multiplicative HW-type models
Low IFSeas and high IFTrnd would suggest damped trend/seasonal models
Once established for a line of products, this can then be automated for an
ongoing basis in a forecast decision support system (FDSS)

19. IFSeas 0,995439
IFTrnd 0,978956
Influence Factors
Step 7: Seasonal Influence & Trend Influence Factors

20. Step 7: Seasonal Influence & Trend Influence Factors

21. Step 7: Seasonal Influence & Trend Influence Factors

22. Step 7: Seasonal Influence & Trend Influence Factors

23. Step 7: Seasonal Influence & Trend Influence Factors

24. Summary of Results

25. Summary of Results

26. Summary of Results

27. Summary of Results

28. The ABC of Automated Forecast Model Profile Selection
Recap
1. Data Prep
2. ANOVA
3. Sources of variability
4. % ETS
5. Seasonal Strength & Trend Strength Factors
6. Seasonal Influence & Trend Influence
7. Seasonal Influence & Trend Influence Factors  Empirical diagram

29. Thank you for your attention!
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