Rule-Based Forecasting: Using Judgment in Time-Series Extrapolation

  • J. Scott Armstrong
  • Monica Adya
  • Fred Collopy
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 30)

Abstract

Rule-Based Forecasting (RBF) is an expert system that uses judgment to develop and apply rules for combining extrapolations. The judgment comes from two sources, forecasting expertise and domain knowledge. Forecasting expertise is based on more than a half century of research. Domain knowledge is obtained in a structured way; one example of domain knowledge is managers’ expectations about trends, which we call “causal forces.” Time series are described in terms of up to 28 conditions, which are used to assign weights to extrapolations. Empirical results on multiple sets of time series show that RBF produces more accurate forecasts than those from traditional extrapolation methods or equal-weights combined extrapolations. RBF is most useful when it is based on good domain knowledge, the domain knowledge is important, the series is well-behaved (such that patterns can be identified), there is a strong trend in the data, and the forecast horizon is long. Under ideal conditions, the error for RBF’s forecasts were one-third less than those for equal-weights combining. When these conditions are absent, RBF will neither improve nor harm forecast accuracy. Some of RBF’s rules can be used with traditional extrapolation procedures. In a series of studies, rules based on causal forces improved the selection of forecasting methods, the structuring of time series, and the assessment of prediction intervals.

Keywords

Accuracy causal forces combining forecasts consistent trends contrary series cycles damped trends decay forces decomposition discontinuities expert systems exponential smoothing extrapolation growth forces inconsistent trends instabilities judgment opposing forces outliers regressing forces reinforcing series start-up series supporting forces 

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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • J. Scott Armstrong
    • 1
  • Monica Adya
    • 2
  • Fred Collopy
    • 3
  1. 1.The Wharton SchoolUniversity of PennsylvaniaUSA
  2. 2.Department of ManagementDePaul UniversityUSA
  3. 3.The Weatherhead School of ManagementCase Western Reserve UniversityUSA

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