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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 …
Automatic Identification Of Time Series Features For Rule-Based Forecasting, Monica Adya, Fred Collopy, J. Scott Armstrong, Miles Kennedy
Automatic Identification Of Time Series Features For Rule-Based Forecasting, Monica Adya, Fred Collopy, J. Scott Armstrong, Miles Kennedy
Monica Adya
Rule-based forecasting (RBF) is an expert system that uses features of time series to select and weight extrapolation techniques. Thus, it is dependent upon the identification of features of the time series. Judgmental coding of these features is expensive and the reliability of the ratings is modest. We developed and automated heuristics to detect six features that had previously been judgmentally identified in RBF: outliers, level shifts, change in basic trend, unstable recent trend, unusual last observation, and functional form. These heuristics rely on simple statistics such as first differences and regression estimates. In general, there was agreement between automated …