An extrapolation procedure used for forecasting. It is a weighted moving average in which the weights are decreased exponentially as data becomes older. For most situations (but not all), it is more accurate than moving averages (Armstrong 2001c). In the past, exponential smoothing was less expensive than a moving average because it used only a few values to summarize the prior data (whereas an *n*-period moving average had to retain all *n* values). The low cost of computer storage has reduced this advantage. When seasonal factors are difficult to measure, moving averages might be preferred to exponential smoothing. For example, a 12-month moving average might be useful in situations with much seasonal variation and less than four years of data. A comprehensive treatment of exponential smoothing is provided in Gardner (1985). See also Holt-Winters exponential smoothing method and state-space model.

- Armstrong, J. S. (2001c), “Extrapolation for time-series
and cross-sectional data,” in J. S. Armstrong (ed.),
*Principles of Forecasting*. Norwell, MA: Kluwer Academic Press. - Gardner, E. S. Jr. (1985), “Exponential smoothing: The
state of the art,”
*Journal of Forecasting*, 4, 1-28.