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An Application Of Rule-Based Forecasting To A Situation Lacking Domain Knowledge, Monica Adya, J. Armstrong, Fred Collopy, Miles Kennedy
An Application Of Rule-Based Forecasting To A Situation Lacking Domain Knowledge, Monica Adya, J. Armstrong, Fred Collopy, Miles Kennedy
Monica Adya
Rule-based forecasting (RBF) uses rules to combine forecasts from simple extrapolation methods. Weights for combining the rules use statistical and domain-based features of time series. RBF was originally developed, tested, and validated only on annual data. For the M3-Competition, three major modifications were made to RBF. First, due to the absence of much in the way of domain knowledge, we prepared the forecasts under the assumption that no domain knowledge was available. This removes what we believe is one of RBF's primary advantages. We had to re-calibrate some of the rules relating to causal forces to allow for this lack …
Rule-Based Forecasting: Using Domain Knowledge In Time Series Extrapolation, J. Armstrong, Monica Adya, Fred Collopy
Rule-Based Forecasting: Using Domain Knowledge In Time Series Extrapolation, J. 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 …