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Note: Rule-Based Forecasting vs. Damped-Trend Exponential Smoothing

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  • Everette S. Gardner

    (Center for Global Manufacturing, College of Business Administration, University of Houston, Houston, Texas 77204-6282)

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    Abstract

    This paper evaluates the ex ante performance of rule-based time series forecasting systems proposed in earlier research. The author shows that comparable performance can be obtained with a simpler alternative, a damped-trend version of exponential smoothing fitted to minimize the Mean-Absolute-Deviation (MAD) criterion. The results suggest that the performance of rule-based systems would be improved through this alternative and that time series forecasters should consider MAD fits in model development.

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    File URL: http://dx.doi.org/10.1287/mnsc.45.8.1169
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 45 (1999)
    Issue (Month): 8 (August)
    Pages: 1169-1176

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    Handle: RePEc:inm:ormnsc:v:45:y:1999:i:8:p:1169-1176

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    Related research

    Keywords: combining forecasts; exponential smoothing; extrapolation; expert systems; judgment; rule-based forecasting;

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    Cited by:
    1. Gardner Jr., Everette S. & Diaz-Saiz, Joaquin, 2008. "Exponential smoothing in the telecommunications data," International Journal of Forecasting, Elsevier, vol. 24(1), pages 170-174.
    2. Taylor, James W., 2007. "Forecasting daily supermarket sales using exponentially weighted quantile regression," European Journal of Operational Research, Elsevier, vol. 178(1), pages 154-167, April.
    3. Bermudez, J.D. & Segura, J.V. & Vercher, E., 2006. "A decision support system methodology for forecasting of time series based on soft computing," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 177-191, November.
    4. Taylor, James W., 2003. "Exponential smoothing with a damped multiplicative trend," International Journal of Forecasting, Elsevier, vol. 19(4), pages 715-725.
    5. Adya, Monica & Collopy, Fred & Armstrong, J. Scott & Kennedy, Miles, 2001. "Automatic identification of time series features for rule-based forecasting," International Journal of Forecasting, Elsevier, vol. 17(2), pages 143-157.

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