Rule-Based Forecasting: Using Judgment in Time-Series Extrapolation
Document Type
Contribution to Book
Language
eng
Format of Original
24 p.; 27 cm
Publication Date
2001
Publisher
Kluwer
Source Publication
Principles of Forecasting: A Handbook for Researchers and Practitioners
Source ISSN
0792379306
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 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 neither improves nor harms 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.
Recommended Citation
Armstrong, J. Scott; Adya, Monica; and Collopy, Fred, "Rule-Based Forecasting: Using Judgment in Time-Series Extrapolation" (2001). Management Faculty Research and Publications. 78.
https://epublications.marquette.edu/mgmt_fac/78
Comments
"Rule-Based Forecasting: Using Judgment in Time-Series Extrapolation," in Principles of Forecasting: The Handbook for Researchers and Practitioners. Ed. Jon Scott Armstrong. Boston: Kluwe, 2001. Publisher Link.
Monica Adya was affiliated with Case Western University at the time of publication.