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| Content Provider | Springer Nature Link |
|---|---|
| Author | Pukenas, Kazimieras |
| Copyright Year | 2013 |
| Abstract | In this paper, it is shown that the block circulant matrix decomposition technique makes the multivariate singular spectrum analysis (M-SSA) a well suited tool for detecting of changes of the correlation structure in non-stationary multivariate time series in the presence of high observational noise levels. The major drawback of M-SSA, that it operates on a large covariance matrix and becomes computationally expensive, can be avoided by reordering the Toeplitz-block covariance matrix into a block Toeplitz matrix, embedding this into a block circulant matrix and efficiently block-diagonalizing this by the means of the Fast Fourier Transform (FFT) using the well known algorithm. The overall degree of synchronization among multiple-channel signals is defined by the synchronization index (the S-estimator) of the rearranged and truncated eigenvalue spectrum. Throughout the experiment, the high capability of the proposed algorithm to detect the lag-synchronized state under the influence of strong noise is validated with simulated data—a network of time series generated by autoregressive models (AR) and a network of coupled chaotic Roessler oscillators. |
| Starting Page | 1289 |
| Ending Page | 1297 |
| Page Count | 9 |
| File Format | |
| ISSN | 0278081X |
| Journal | Circuits, Systems, and Signal Processing |
| Volume Number | 33 |
| Issue Number | 4 |
| e-ISSN | 15315878 |
| Language | English |
| Publisher | Springer US |
| Publisher Date | 2013-10-22 |
| Publisher Place | Boston |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Phase space Multivariate singular spectrum analysis Eigenvalue spectrum Block Toeplitz matrix Block circulant matrix Fast Fourier Transform Circuits and Systems Electrical Engineering Signal, Image and Speech Processing Electronics and Microelectronics, Instrumentation |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Signal Processing |
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