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Reconstructing multi-mode networks from multivariate time series

Urheber*innen

Gao,  Z.-K.
External Organizations;

Yang,  Y.-X.
External Organizations;

Dang,  W.-D.
External Organizations;

Cai,  Q.
External Organizations;

Wang,  Z.
External Organizations;

/persons/resource/Marwan

Marwan,  Norbert
Potsdam Institute for Climate Impact Research;

Boccaletti,  S.
External Organizations;

/persons/resource/Juergen.Kurths

Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;

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Zitation

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


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_22364
Zusammenfassung
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.