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Forecasting When Pattern Changes Occur Beyond the Historical Data

Author

Listed:
  • Robert Carbone

    (Faculty of Management, McGill University, Montreal, Quebec, Canada)

  • Spyros Makridakis

    (INSEAD, Fontainebleau, France)

Abstract

Forecasting 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 historical 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 comparisons show the superiority of the proposed approach over the most accurate methods in the M-Competition.

Suggested Citation

  • Robert Carbone & Spyros Makridakis, 1986. "Forecasting When Pattern Changes Occur Beyond the Historical Data," Management Science, INFORMS, vol. 32(3), pages 257-271, March.
    • Handle: RePEc:inm:ormnsc:v:32:y:1986:i:3:p:257-271
    DOI: 10.1287/mnsc.32.3.257
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    Citations

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    Cited by:

    1. Lawrence, M. & O'Connor, M., 1996. "Judgement or models: The importance of task differences," Omega, Elsevier, vol. 24(3), pages 245-254, June.
    2. Mastroeni, Loretta & Mazzoccoli, Alessandro & Vellucci, Pierluigi, 2024. "Studying the impact of fluctuations, spikes and rare events in time series through a wavelet entropy predictability measure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 641(C).
    3. 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.
    4. Sterman, John., 1986. "Expectation formation in behavioral simulation models," Working papers 1826-86., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    5. Remus, William & O'Connor, Marcus & Griggs, Kenneth, 1995. "Does reliable information improve the accuracy of judgmental forecasts?," International Journal of Forecasting, Elsevier, vol. 11(2), pages 285-293, June.

    More about this item

    Keywords

    forecasting/time series;

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