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

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  • Fred Collopy

    (The Weatherhead School of Management, Case Western Reserve University, Cleveland, Ohio 44106)

  • J. Scott Armstrong

    (The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

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.

Suggested Citation

  • Fred Collopy & J. Scott Armstrong, 1992. "Rule-Based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations," Management Science, INFORMS, vol. 38(10), pages 1394-1414, October.
    • Handle: RePEc:inm:ormnsc:v:38:y:1992:i:10:p:1394-1414
    DOI: 10.1287/mnsc.38.10.1394
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    References listed on IDEAS

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    1. JS Armstrong & Fred Collopy, 2004. "Causal Forces: Structuring Knowledge for Time-series Extrapolation," General Economics and Teaching 0412003, University Library of Munich, Germany.
    2. Fildes, Robert & Lusk, Edward J, 1984. "The choice of a forecasting model," Omega, Elsevier, vol. 12(5), pages 427-435.
    3. Robert Carbone & Spyros Makridakis, 1986. "Forecasting When Pattern Changes Occur Beyond the Historical Data," Management Science, INFORMS, vol. 32(3), pages 257-271, March.
    4. Scott Armstrong, J., 1988. "Research needs in forecasting," International Journal of Forecasting, Elsevier, vol. 4(3), pages 449-465.
    5. Robert Carbone & JS Armstrong, 2004. "Evaluation of Extrapolative Forecasting Methods: Results of a Survey of Academicians and Practitioners," General Economics and Teaching 0412008, University Library of Munich, Germany.
    6. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    7. Sanders, NR & Ritzman, LP, 1990. "Improving short-term forecasts," Omega, Elsevier, vol. 18(4), pages 365-373.
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