An Application of Rule-Based Forecasting to a Situation Lacking Domain Knowledge

6 Pages Posted: 7 Feb 2005 Last revised: 31 Dec 2011

See all articles by Monica Adya

Monica Adya

Marquette University - College of Business Administration

J. Scott Armstrong

University of Pennsylvania - Marketing Department

Fred Collopy

Case Western Reserve University - Department of Information Systems

Miles Kennedy

Independent Author

Abstract

Rule-based forecasting (RBF) uses rules to combine forecasts from simple extrapolation methods. Weights for combining the rules use statistical and domain-based features of time series. RBF was originally developed, tested, and validated only on annual data. For the M3-Competition, three major modifications were made to RBF. First, due to the absence of much in the way of domain knowledge, we prepared the forecasts under the assumption that no domain knowledge was available. This removes what we believe is one of RBF's primary advantages. We had to re-calibrate some of the rules relating to causal forces to allow for this lack of domain knowledge. Second, automatic identification procedures were used for six time-series features that had previously been identified using judgment. This was done to reduce cost and improve reliability. Third, we simplified the rule-base by removing one method from the four that were used in the original implementation. Although this resulted in some loss in accuracy, it reduced the number of rules in the rule-base from 99 to 64. This version of RBF still benefits from the use of prior findings on extrapolation, so we expected that it would be substantially more accurate than the random walk and somewhat more accurate than equal weights combining. Because most of the previous work on RBF was done using annual data, we especially expected it to perform well with annual data.

Keywords: Forecast, rule-based forecasting, RBF

Suggested Citation

Adya, Monica and Armstrong, J. Scott and Collopy, Fred and Kennedy, Miles, An Application of Rule-Based Forecasting to a Situation Lacking Domain Knowledge. International Journal of Forecasting, Vol. 16, pp. 477-484, 2000. Available at SSRN: https://ssrn.com/abstract=662621

Monica Adya

Marquette University - College of Business Administration ( email )

P.O. Box 1881
Milwaukee, WI 53201-1881
United States

J. Scott Armstrong (Contact Author)

University of Pennsylvania - Marketing Department ( email )

700 Jon M. Huntsman Hall
3730 Walnut Street
Philadelphia, PA 19104-6340
United States
215-898-5087 (Phone)
215-898-2534 (Fax)

HOME PAGE: http://marketing.wharton.upenn.edu/people/faculty/armstrong.cfm

Fred Collopy

Case Western Reserve University - Department of Information Systems ( email )

10900 Euclid Ave.
Cleveland, OH 44106-7235
United States

Miles Kennedy

Independent Author

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