Algorithm for the Detection of Changes of the Correlation Structure in Multivariate Time Series
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| Title | Algorithm for the Detection of Changes of the Correlation Structure in Multivariate Time Series |
| Authors | |
| 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 |
| Publisher | Kaunas University of Technology |
| Date | 2012-10-26 |
| Source | Elektronika ir elektrotechnika Vol 18, No 8 (2012) |
| Rights | Autorių teisės yra apibrėžtos Lietuvos Respublikos autorių teisių ir gretutinių teisių įstatymo 4-37 straipsniuose. |