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

Alshammri, Fayed and Pan, Jiazhu (2021) Moving dynamic principal component analysis for non-stationary multivariate time series. Computational Statistics, 36 (3). pp. 2247-2287. ISSN 1613-9658 (https://doi.org/10.1007/s00180-021-01081-8)

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Abstract

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

ORCID iDs

Alshammri, Fayed and Pan, Jiazhu ORCID logoORCID: https://orcid.org/0000-0001-7346-2052;
CORE (COnnecting REpositories)