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


  • J. S. Armstrong

    (The Wharton School)


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.

Suggested Citation

  • J. S. Armstrong, 2005. "Decomposition by Causal Forces: A Procedure for Forecasting Complex Time Series," General Economics and Teaching 0502015, University Library of Munich, Germany.
  • Handle: RePEc:wpa:wuwpgt:0502015
    Note: Type of Document - pdf; pages: 21

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    References listed on IDEAS

    1. JS Armstrong & Fred Collopy, 2004. "Causal Forces: Structuring Knowledge for Time-series Extrapolation," General Economics and Teaching 0412003, University Library of Munich, Germany.
    2. Fred Collopy & J. Scott Armstrong, 1992. "Rule-Based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations," Management Science, INFORMS, vol. 38(10), pages 1394-1414, October.
    3. Ernst R. Berndt & Neal J. Rappaport, 2001. "Price and Quality of Desktop and Mobile Personal Computers: A Quarter-Century Historical Overview," American Economic Review, American Economic Association, vol. 91(2), pages 268-273, May.
    4. 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.
    5. Armstrong, J Scott & Collopy, Fred, 2001. "Identification of Asymmetric Prediction Intervals through Causal Forces," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(4), pages 273-283, July.
    6. JS Armstrong & Fred Collopy, 2004. "Integration of Statistical Methods and Judgment for Time Series," General Economics and Teaching 0412024, University Library of Munich, Germany.
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    Cited by:

    1. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    2. Kirshners Arnis & Borisov Arkady, 2012. "A Comparative Analysis of Short Time Series Processing Methods," Information Technology and Management Science, Sciendo, vol. 15(1), pages 65-69, December.
    3. Anqiang Huang & Kin Keung Lai & Han Qiao & Shouyang Wang & Zhenji Zhang, 2018. "Does Interval Knowledge Sharpen Forecasting Models? Evidence from China’s Typical Ports," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 467-483, March.
    4. Armstrong, J. Scott, 2006. "Findings from evidence-based forecasting: Methods for reducing forecast error," International Journal of Forecasting, Elsevier, vol. 22(3), pages 583-598.
    5. Germán Rubio Guerrero, 2017. "Perspectiva multivariante de los pronósticos en las pymes industriales de Ibagué (Colombia)," Revista Facultad de Ciencias Económicas, Universidad Militar Nueva Granada, vol. 25(2), pages 25-40, September.


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