| The accuracy of extrapolation methods varies greatly from one time series to another and across forecast horizons. Selecting the most accurate method for a particular series and horizon is one of the forecaster's principal tasks.;This dissertation discusses the development and validation of a rule-based system to produce extrapolation forecasts. Rule-based forecasting uses domain knowledge, forecasting expertise, the features of each series, and research to combine quantitative forecasts. The rule-base was developed using a review of the literature on forecasting accuracy, a survey of forecasting experts, direct assessments from five experts in forecasting, and protocol analyses from the same five experts. The resulting rule-base consisted of 87 rules, which make use of 18 features to weight four component methods to produce forecasts.;An error measure (the relative absolute error) is developed in this dissertation to aid in the calibration of rule-based forecasting systems. The measure is shown to be sensitive, reliable, and valid.;Rule-based forecasting produced substantially more accurate forecasts than could be obtained by the best prior formal approach, which was to combine forecasts using equal weights. For six-year-ahead ex ante forecasts of 90 annual series, the median absolute percentage error for rule-based forecasting was 42% less than that from combining forecasts. The improvement in accuracy of the rule-based forecasts over combining was significant at p $<$.01. Rule-based forecasting was most accurate in situations involving good domain expertise, stability, significant trends, and low uncertainty. |
