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

Fayed Alshammri, Jiazhu Pan

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
90 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)2247-2287
Number of pages41
JournalComputational Statistics
Issue number3
Early online date7 Mar 2021
Publication statusPublished - 30 Sept 2021


  • dimension reduction
  • Eigen analysis
  • moving cross co-variance
  • moving cross correlation
  • multivariate time series
  • non-stationary data


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