EWMA control charts for autoregressive processes

Many processes must be monitored by using observations that are correlated. An approach called algorithmic statistical process control call be employe d in such situations. This involves fitting an autoregressive/moving averag e 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 con trol chart. In this paper we consider using an exponentially weighted movin g average (EWMA) chart for monitoring the residuals from an autoregressive model. We present a computational method for finding the out-of-control ave rage 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 find ing the control limits of an EWMA chart for monitoring residuals from an au toregressive model that will provide an acceptable out-of-control ARL. A co mputer program for the needed calculations is provided via the World Wide W eb.