Monitoring Variability and Analyzing Multivariate Autocorrelated Processes
Traditional multivariate quality control charts are based on independent observations. In this paper, we explain how to extend univariate residual charts to multivariate cases and how to combine the traditional statistical process control (SPC) approaches to monitor changes in process variability in a dynamic environment. We propose using Alt's (1984) W chart on vector autoregressive (VAR) residuals to monitor the variability for multivariate processes in the presence of autocorrelation. We study examples jointly using the Hotelling T2 chart on VAR residuals, the W chart, and the Portmanteau test to diagnose the types of shift in process parameters.
ABSTRACT The aim of this research was to analyse the different results that can be achieved using support vector machines (SVM) to forecast the weekly change movement of different simulated markets. The markets are developed by a GARCH model based on the SP also results are good in trend simulated markets. Copyright © 2014 John Wiley & Sons, Ltd.