Rule-Based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations
AbstractThis paper examines the feasibility of rule-based forecasting, a procedure that applies forecasting expertise and domain knowledge to produce forecasts according to features of the data. We developed a rule base to make annual extrapolation forecasts for economic and demographic time series. The development of the rule base drew upon protocol analyses of five experts on forecasting methods. This rule base, consisting of 99 rules, combined forecasts from four extrapolation methods (the random walk, regression, Brown's linear exponential smoothing, and Holt's exponential smoothing) according to rules using 18 features of time series. For one-year ahead ex ante forecasts of 90 annual series, the median absolute percentage error (MdAPE) for rule-based forecasting was 13% less than that from equally-weighted combined forecasts. For six-year ahead ex ante forecasts, rule-based forecasting had a MdAPE that was 42% less. The improvement in accuracy of the rule-based forecasts over equally-weighted combined forecasts was statistically significant. Rule-based forecasting was more accurate than equal-weights combining in situations involving significant trends, low uncertainty, stability, and good domain expertise.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by INFORMS in its journal Management Science.
Volume (Year): 38 (1992)
Issue (Month): 10 (October)
combining forecasts; expert systems; extrapolation; judgment; rule base; rule-based forecasting;
Other versions of this item:
- Fred Collopy & JS Armstrong, 2004. "Rule-Based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations," General Economics and Teaching 0412004, EconWPA.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Fildes, Robert & Lusk, Edward J, 1984. "The choice of a forecasting model," Omega, Elsevier, vol. 12(5), pages 427-435.
- Scott Armstrong, J., 1988. "Research needs in forecasting," International Journal of Forecasting, Elsevier, vol. 4(3), pages 449-465.
- JS Armstrong & Fred Collopy, 2004. "Causal Forces: Structuring Knowledge for Time-series Extrapolation," General Economics and Teaching 0412003, EconWPA.
- Sanders, NR & Ritzman, LP, 1990. "Improving short-term forecasts," Omega, Elsevier, vol. 18(4), pages 365-373.
- Robert Carbone & JS Armstrong, 2004. "Evaluation of Extrapolative Forecasting Methods: Results of a Survey of Academicians and Practitioners," General Economics and Teaching 0412008, EconWPA.
- Robert Carbone & Spyros Makridakis, 1986. "Forecasting When Pattern Changes Occur Beyond the Historical Data," Management Science, INFORMS, vol. 32(3), pages 257-271, March.
- Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
This item has more than 25 citations. To prevent cluttering this page, these citations are listed on a separate page. reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Mirko Janc).
If references are entirely missing, you can add them using this form.