Some properties of a simple moving average when applied to forecasting a time series

F R Johnston (), J E Boyland, M Meadows and E Shale
Additional contact information
F R Johnston: University of Warwick
J E Boyland: Buckinghamshire University College
M Meadows: University of Warwick
E Shale: University of Warwick

Journal of the Operational Research Society, 1999, vol. 50, issue 12, 1267-1271

Abstract: Abstract Simple (equally weighted) moving averages are frequently used to estimate the current level of a time series, with this value being projected as a forecast for future observations. A key measure of the effectiveness of the method is the sampling error of the estimator, which this paper defines in terms of characteristics of the data. This enables the optimal length of the average for any steady state model to be established and the lead time forecast error derived. A comparison of the performance of a simple moving average (SMA) with an exponentially weighted moving average (EWMA) is made. It is shown that, for a steady state model, the variance of the forecast error is typically less than 3% higher than the appropriate EWMA. This relatively small difference may explain the inconclusive results from the empirical studies about the relative predictive performance of the two methods.

Keywords: forecasting; time series; moving averages; exponentially weighted moving averages (search for similar items in EconPapers)
Date: 1999
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Citations: View citations in EconPapers (9)

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DOI: 10.1057/palgrave.jors.2600823

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