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Expert Systems For Forecasting, Fred Collopy, Monica Adya, J. Scott Armstrong
Expert Systems For Forecasting, Fred Collopy, Monica Adya, J. Scott Armstrong
Monica Adya
Expert systems use rules to represent experts’ reasoning in solving problems. The rules are based on knowledge about methods and the problem domain. To acquire knowledge for an expert system, one should rely on a variety of sources, such as textbooks, research papers, interviews, surveys, and protocol analysis. Protocol analysis is especially useful if the area to be modeled is complex or if experts lack an awareness of their processes. Expert systems should be easy to use, incorporate the best available knowledge, and reveal the reasoning behind the recommendations they make. In forecasting, the most promising applications of expert systems …
Rule-Based Forecasting: Using Judgment In Time-Series Extrapolation, J. Scott Armstrong, Monica Adya, Fred Collopy
Rule-Based Forecasting: Using Judgment In Time-Series Extrapolation, J. Scott Armstrong, Monica Adya, Fred Collopy
Monica Adya
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 …