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

F. R. Johnston, J. E. Boyland, M. Meadows, E. Shale

Research output: Contribution to journalArticlepeer-review

75 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1267-1271
Number of pages5
JournalJournal of the Operational Research Society
Volume50
Issue number12
DOIs
Publication statusPublished - Dec 1999
Externally publishedYes

Keywords

  • Exponentially weighted moving averages
  • Forecasting
  • Moving averages
  • Time series

ASJC Scopus subject areas

  • Management Information Systems
  • Strategy and Management
  • Management Science and Operations Research
  • Marketing

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