Model-based Multivariate Monitoring Charts for Autocorrelated Processes

Autocorrelation or nonstationarity may seriously impact the performance of conventional Hotelling's T-2 charts. We suggest modeling processes with multivariate autoregressive integrated moving average time series models and propose two model-based monitoring charts. One monitors the predicted value and provides information about the need for mean adjustments. The other is a Hotelling's T-2 control chart applied to the residuals. The average run length performance of the residual-based Hotelling's T-2 chart is compared with the observed data-based Hotelling's T-2 chart for a group of first-order vector autoregressive models. We show that the new chart in most cases performs well. Copyright (c) 2013 John Wiley & Sons, Ltd.