Support vector regression based residual MCUSUM control chart for autocorrelated process

Traditional control charts assume that processes are serially independent, and autocorrelation among variables makes them unreliable. To handle this problem alternative charts estimate the time series structure of the process and use residuals for control. While in previous studies, estimation is performed using classical statistical methods or artificial neural networks, this study proposes to apply support vector regression (SVR) method for construction of a residuals Multivariate Cumulative Sum (MCUSUM) control chart, for monitoring changes in the process mean vector. Using simulated data, analysis and comparison of the proposed control chart with other charts show that SVR-based control chart is more effective in detecting small shifts in the mean vector. This fact makes the proposed chart a very promising method since the MCUSUM chart is, in practice, designed to detect small shifts in the process parameters.