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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 …
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 …