Algorithm for the Detection of Changes of the Correlation Structure in Multivariate Time Series

K. Pukenas

Abstract


In this research, an improved algorithm for the detection of changes of the correlation structure in multivariate time series is proposed. The starting point of the technique is a covariance matrix whose entries are the largest entries of a cross-covariance matrix which is composed of all pairs of the time series reconstructed to an M-dimensional phase space. Principal component analysis is performed on this maximized cross-covariance matrix, and the overall degree of synchronization among multiple-channel signals is defined, by synchronization index, as the Shannon entropy of the eigenvalue spectrum. Throughout the experiment, the effectiveness of the proposed algorithm is validated with simulated data – a network of time series generated by autoregressive models and a network of coupled chaotic Roessler oscillators.

DOI: http://dx.doi.org/10.5755/j01.eee.18.8.2625


Keywords


Covariance matrix; entropy; principal component analysis

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Print ISSN: 1392-1215
Online ISSN: 2029-5731