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

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.