Reconstructing multi-mode networks from multivariate time series

Gao, Z.-K.; Yang, Y.-X.; Dang, W.-D.; Cai, Q.; Wang, Z.; Marwan, Norbert; Boccaletti, S.; Kurths, Jürgen

doi:10.1209/0295-5075/119/50008

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Reconstructing multi-mode networks from multivariate time series

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Gao, Z.-K.
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Yang, Y.-X.
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Dang, W.-D.
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Cai, Q.
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Wang, Z.
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Marwan, Norbert
Potsdam Institute for Climate Impact Research;

Boccaletti, S.
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/persons/resource/Juergen.Kurths

Kurths, Jürgen
Potsdam Institute for Climate Impact Research;

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Citation

Gao, Z.-K., Yang, Y.-X., Dang, W.-D., Cai, Q., Wang, Z., Marwan, N., Boccaletti, S., Kurths, J. (2017): Reconstructing multi-mode networks from multivariate time series. - EPL (Europhysics Letters), 119, 5, 50008.
https://doi.org/10.1209/0295-5075/119/50008

Cite as: https://publications.pik-potsdam.de/pubman/item/item_22364

Abstract

Unveiling the dynamics hidden in multivariate time series is a task of the utmost importance in a broad variety of areas in physics. We here propose a method that leads to the construction of a novel functional network, a multi-mode weighted graph combined with an empirical mode decomposition, and to the realization of multi-information fusion of multivariate time series. The method is illustrated in a couple of successful applications (a multi-phase flow and an epileptic electro-encephalogram), which demonstrate its powerfulness in revealing the dynamical behaviors underlying the transitions of different flow patterns, and enabling to differentiate brain states of seizure and non-seizure.