Automatic feature identification and graphical support in rule-based forecasting: a comparison

Automatic feature identification and graphical support in rule-based forecasting: a comparison

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Article ID: iaor19972361
Country: Netherlands
Volume: 12
Issue: 5
Start Page Number: 495
End Page Number: 512
Publication Date: Dec 1996
Journal: International Journal of Forecasting
Authors: , ,
Keywords: forecasting: applications
Abstract:

The authors 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 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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