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EWMA control charts for autoregressive processes

Author

Listed:
  • A B Koehler

    (Miami University)

  • N B Marks

    (Miami University)

  • R T O'connell

    (Miami University)

Abstract

Many processes must be monitored by using observations that are correlated. An approach called algorithmic statistical process control can be employed in such situations. This involves fitting an autoregressive/moving average time series model to the data. Forecasts obtained from the model are used for active control, while the forecast errors are monitored by using a control chart. In this paper we consider using an exponentially weighted moving average (EWMA) chart for monitoring the residuals from an autoregressive model. We present a computational method for finding the out-of-control average run length (ARL) for such a control chart when the process mean shifts. As an application, we suggest a procedure and provide an example for finding the control limits of an EWMA chart for monitoring residuals from an autoregressive model that will provide an acceptable out-of-control ARL. A computer program for the needed calculations is provided via the World Wide Web.

Suggested Citation

  • A B Koehler & N B Marks & R T O'connell, 2001. "EWMA control charts for autoregressive processes," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(6), pages 699-707, June.
    • Handle: RePEc:pal:jorsoc:v:52:y:2001:i:6:d:10.1057_palgrave.jors.2601140
    DOI: 10.1057/palgrave.jors.2601140
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    Cited by:

    1. Vermaat, M.B. & van der Meulen, F.H. & Does, R.J.M.M., 2008. "Asymptotic behavior of the variance of the EWMA statistic for autoregressive processes," Statistics & Probability Letters, Elsevier, vol. 78(12), pages 1673-1682, September.
    2. Ord, J. Keith & Koehler, Anne B. & Snyder, Ralph D. & Hyndman, Rob J., 2009. "Monitoring processes with changing variances," International Journal of Forecasting, Elsevier, vol. 25(3), pages 518-525, July.

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