AUTOMATIC FEATURE IDENTIFICATION AND GRAPHICAL SUPPORT IN RULE-BASED FORECASTING - A COMPARISON

We examined automatic feature identification and graphical support in rule-based expert systems for forecasting. The rule-based expert forec asting system (RBEFS) includes predefined rules to automatically ident ify features of a time series and selects the extrapolation method to be used. The system call also integrate managerial judgment using a gr aphical interface that allows a user to view alternate extrapolation m ethods two at a time. The use of the RBEFS led to a significant improv ement 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 equal ly weighted combined forecasts and a 33% improvement over the random w alk. 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 o f an earlier rule-based expert system which required human judgments a bout some features of the time series data. The results of the compari son of the two rule-based expert systems showed no significant differe nces between them.