Rule-Based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations

Management Science, Vol. 38, No. 10, pp. 1394-1414, 1992

25 Pages Posted: 8 Feb 2005 Last revised: 1 Jan 2012

See all articles by Fred Collopy

Fred Collopy

Case Western Reserve University - Department of Information Systems

J. Scott Armstrong

University of Pennsylvania - Marketing Department

Abstract

This paper examines the feasibility of rule-based forecasting, a procedure that applies forecasting expertise and domain knowledge to produce forecasts according to features of the data. We developed a rule base to make annual extrapolation forecasts for economic and demographic time series. The development of the rule base drew upon protocol analyses of five experts on forecasting methods. This rule base, consisting of 99 rules, combined forecasts from four extrapolation methods (the random walk, regression, Brown's linear exponential smoothing, and Holt's exponential smoothing) according to rules using 18 features of time series. For one-year ahead ex ante forecasts of 90 annual series, the median absolute percentage error (MdAPE) for rule-based forecasting was 13% less than that from equally-weighted combined forecasts. For six-year ahead ex ante forecasts, rule-based forecasting had a MdAPE that was 42% less. The improvement in accuracy of the rule-based forecasts over equally-weighted combined forecasts was statistically significant. Rule-based forecasting was more accurate than equal-weights combining in situations involving significant trends, low uncertainty, stability, and good domain expertise.

Keywords: Rule-based forecasting, forecasting, time series, research

Suggested Citation

Collopy, Fred and Armstrong, J. Scott, Rule-Based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations. Management Science, Vol. 38, No. 10, pp. 1394-1414, 1992. Available at SSRN: https://ssrn.com/abstract=663646

Fred Collopy

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

10900 Euclid Ave.
Cleveland, OH 44106-7235
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

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