Decomposition by Causal Forces: A Procedure for Forecasting Complex Time Series

Posted: 7 Feb 2005 Last revised: 1 Jan 2012

See all articles by J. Scott Armstrong

J. Scott Armstrong

University of Pennsylvania - Marketing Department

Fred Collopy

Case Western Reserve University - Department of Information Systems

Thomas Yokum

Angelo State University - College of Business and Professional Studies

Abstract

Causal forces are a way of summarizing forecasters' expectations about what will happen to a time series in the future. Contrary to the common assumption for extrapolation, time series are not always subject to consistent forces that point in the same direction. Some are affected by conflicting causal forces; we refer to these as complex times series. It would seem that forecasting these times series would be easier if one could decompose the series to eliminate the effects of the conflicts. Given forecasts subject to high uncertainty, we hypothesized that a time series could be effectively decomposed under two conditions: 1) if domain knowledge can be used to structure the problem so that causal forces are consistent for two or more component series, and 2) when it is possible to obtain relatively accurate forecasts for each component. Forecast accuracy for the components can be assessed by testing how well they can be forecast on early hold-out data. When such data are not available, historical variability may be an adequate substitute. We tested decomposition by causal forces on 12 complex annual time series for automobile accidents, airline accidents, personal computer sales, airline revenues, and cigarette production. The length of these series ranged from 16 years for airline revenues to 56 years for highway safety data. We made forecasts for one to ten horizons, obtaining 800 forecasts through successive updating. For nine series in which the conditions were completely or partially met, the forecast error (MdAPE) was reduced by more than half. For three series in which the conditions were not met, decomposition by causal forces had little effect on accuracy.

Keywords: Airline accidents, extrapolation, Holt's exponential smoothing, model formulation, personal computers

Suggested Citation

Armstrong, J. Scott and Collopy, Fred and Yokum, Thomas, Decomposition by Causal Forces: A Procedure for Forecasting Complex Time Series. International Journal of Forecasting, Forthcoming. Available at SSRN: https://ssrn.com/abstract=662541

J. Scott Armstrong (Contact Author)

University of Pennsylvania - Marketing Department ( email )

700 Jon M. Huntsman Hall
3730 Walnut Street
Philadelphia, PA 19104-6340
United States
215-898-5087 (Phone)
215-898-2534 (Fax)

HOME PAGE: http://marketing.wharton.upenn.edu/people/faculty/armstrong.cfm

Fred Collopy

Case Western Reserve University - Department of Information Systems ( email )

10900 Euclid Ave.
Cleveland, OH 44106-7235
United States

Thomas Yokum

Angelo State University - College of Business and Professional Studies ( email )

Rassman Building
San Antonio, TX 76909
325-942-2383 (Phone)
325-655-7807 (Fax)

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