Time Series Control Charts in the Presence of Model Uncertainty

Abstract

Time series control charts are popular methods for statistical process control of autocorrelated processes. In order to implement these methods, however, a time series model of the process is required. Since time series models must always be estimated from process data, model estimation errors are unavoidable. In the presence of modeling errors, time series control charts that are designed under the assumption of a perfect model may have an actual in-control average run length that is substantially shorter than desired. This paper presents a method for incorporating model uncertainty information into the design of time series control charts to provide a level of robustness with respect to modeling errors. The focus is on exponentially weighted moving average charts and Shewhart individual charts applied to the time series residuals.