Exponential Smoothing as a Special Case of a Linear Stochastic System

S. M. Pandit and S. M. Wu
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S. M. Pandit: University of Wisconsin, Madison, Wisconsin
S. M. Wu: University of Wisconsin, Madison, Wisconsin

Operations Research, 1974, vol. 22, issue 4, 868-879

Abstract: This paper derives a uniformly-sampled-autoregressive-moving-average (USAM) model for a second-order linear stochastic system, shows that exponential smoothing is a limiting case of the USAM model, and discusses the optimal value of the exponential-smoothing parameter and its sensitivity to mean-squared error of prediction. The USAM model is interpreted as a first-order system with first-order feedback; its limiting behavior explains why many business, economic, and quality-control systems are predicted well by exponential smoothing. The results are illustrated by examples of real-life data from IBM stock prices, and quality-control measurements of an automatic screw-machine operation.

Date: 1974
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:22:y:1974:i:4:p:868-879

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