Multioutput Least Square SVR Based Multivariate EWMA Control Chart

Abstract

Multioutput least square SVR has ability to remove serial correlation of process by mapping multivariate input space to multivariate output space. The aim of this research is to propose multioutput least squares SVR based multivariate EWMA control chart to monitor small change of multivariate autocorrelated process. VARMA model with additive and innovative outliers are generated to investigate the performance of proposed control chart. Simulation studies empirically show that multioutput least squares SVR based multivariate EWMA control chart detect either single or consecutive additive outlier takes place at different time in each variable accurately. On the contrary, single innovative outlier in each variable that occurs either at different time or at the same time is detected by multioutput least squares SVR based multivariate EWMA control chart as double out-of control signals.