Volume 119, Number 5, September 2017
|Number of page(s)||5|
|Published online||24 November 2017|
Reconstructing multi-mode networks from multivariate time series
1 School of Electrical and Information Engineering, Tianjin University - Tianjin 300072, China
2 Center for OPTical IMagery Analysis and Learning, Northwestern Polytechnical University - Xi'an 710072, China
3 Potsdam Institute for Climate Impact Research - Telegraphenberg A31, 14473 Potsdam, Germany
4 Institute of Complex Systems - Via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy
Received: 13 November 2017
Accepted: 14 November 2017
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.
PACS: 05.45.Tp – Time series analysis / 64.60.aq – Networks
© EPLA, 2017
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.