Automatic feature identification and graphical support in rule-based forecasting: a comparison |
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Authors: | Robert J Vokurka Benito E Flores Stephen L Pearce |
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Institution: | Department of Business Analysis, Texas A&M University, College Station, TX 77843-4217, USA |
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Abstract: | We examined automatic feature identification and graphical support in rule-based expert systems for forecasting. The rule-based expert forecasting system (RBEFS) includes predefined rules to automatically identify features of a time series and selects the extrapolation method to be used. The system can also integrate managerial judgment using a graphical interface that allows a user to view alternate extrapolation methods two at a time. The use of the RBEFS led to a significant improvement in accuracy compared to equal-weight combinations of forecasts. Further improvement were achieved with the user interface. For 6-year ahead ex ante forecasts, the rule-based expert forecasting system has a median absolute percentage error (MdAPE) 15% less than that of equally weighted combined forecasts and a 33% improvement over the random walk. The user adjusted forecasts had a MdAPE 20% less than that of the expert system. The results of the system are also compared to those of an earlier rule-based expert system which required human judgments about some features of the time series data. The results of the comparison of the two rule-based expert systems showed no significant differences between them. |
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Keywords: | Combining forecasts Extrapolation Expert systems Judgment Rule-based forecasting |
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