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Forecasting when pattern changes occur beyond the historical data

dc.contributor.authorCarbone, Robert
dc.contributor.authorMakridakis, Spyros
dc.identifier.citationManagement Scienceen_UK
dc.description.abstractForecasting methods currently available assume that established patterns or relationships will not change during the post-sample forecasting phase. This, however, is not a realistic assumption for business and economic series. This paper describes a new approach to forecasting which takes into account possible pattern changes beyond the histoncal data. This approach is based on the development of two models: one short, the other long term. These models are then reconciled to produce the final forecasts by setting certain parameters as a function of the number, extent, and duration of pattern changes that have occurred in the past. The proposed method has been applied to the 111 series used in the M-Competition. Post-sample forecasting accuracy compansons show the superiority of the proposed approach over the most accurate methods in the A/-Competition.en_UK
dc.relation.ispartofseries;Vol. 32. No 3
dc.relation.ispartofseriesForecasting/Time Series;
dc.rightsCopyright © 1986. The Institute of Management Science.en_UK
dc.subjectResearch Subject Categories::SOCIAL SCIENCES::Business and economicsen_UK
dc.titleForecasting when pattern changes occur beyond the historical dataen_UK

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Copyright © 1986. The Institute of Management Science.
Except where otherwise noted, this item's license is described as Copyright © 1986. The Institute of Management Science.