Moving dynamic principal component analysis for non-stationary multivariate time series

Abstract This paper proposes an extension of principal component analysis to non-stationary multivariate time series data. A criterion for determining the number final retained components is proposed. An advance correlation matrix developed evaluate dynamic relationships among chosen components. The theoretical properties proposed method are given. Many simulation experiments show our approach performs well on both stationary and Real data examples also presented as illustrations. We develop four packages using statistical software R that contain needed functions obtain assess results method.