Parameter Selection for a Robust Tracking Signal

摘要

Tracking signals use past forecast errors to monitor and control a forecasting process. In this study, the cumulative-sum tracking signal and the smoothed-error tracking signal are evaluated on their ability to aid in shift (process upset) detection. The moving-centerline EWMA control chart technique is coupled with these tracking signals to enhance the monitoring of autocorrelated processes. The analysis characterizes two prevalent time series models: AR(1) and ARMA(1,1). The goal of this paper is to explore the capabilities of the tracking signals and the moving-centerline EWMA when the smoothing constants are varied and a shift is introduced into the process. The tracking signals are evaluated based on average run length (ARL) and false alarm rate (FA).