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

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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.
Original languageEnglish
Number of pages41
JournalComputational Statistics
Early online date7 Mar 2021
DOIs
Publication statusE-pub ahead of print - 7 Mar 2021

Keywords

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

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