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A New Look at Higher-Order Exponential Smoothing for Forecasting

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  • M. L. Goodman

    (AT&T Long Lines, 130 John Street, New York, New York)

Abstract

This paper applies residual analysis to build up improved forecasts from exponential smoothing models. The resulting model is not unknown, but the method of achieving it is more straightforward and intuitive than existing methods. The paper also shows that the model is identical to IMA( k , k ) forecasting models.

Suggested Citation

  • M. L. Goodman, 1974. "A New Look at Higher-Order Exponential Smoothing for Forecasting," Operations Research, INFORMS, vol. 22(4), pages 880-888, August.
    • Handle: RePEc:inm:oropre:v:22:y:1974:i:4:p:880-888
    DOI: 10.1287/opre.22.4.880
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    Cited by:

    1. Zhang, Xiaolong, 2004. "The impact of forecasting methods on the bullwhip effect," International Journal of Production Economics, Elsevier, vol. 88(1), pages 15-27, March.
    2. Sargent, Jonathan & Bedford, Anthony, 2010. "Improving Australian Football League player performance forecasts using optimized nonlinear smoothing," International Journal of Forecasting, Elsevier, vol. 26(3), pages 489-497, July.
    3. Cree S. Dawson & Charles J. McCallum & R. Bradford Murphy & Eric Wolman, 2000. "Operations Research at Bell Laboratories through the 1970s: Part III," Operations Research, INFORMS, vol. 48(4), pages 517-526, August.
    4. Reham Alhindawi & Yousef Abu Nahleh & Arun Kumar & Nirajan Shiwakoti, 2020. "Projection of Greenhouse Gas Emissions for the Road Transport Sector Based on Multivariate Regression and the Double Exponential Smoothing Model," Sustainability, MDPI, vol. 12(21), pages 1-18, November.

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