Differ multivariate timeseries from each other based on a simple multiplex visibility graphs technique

Abstract

In this brief paper, based on multiplex visibility graphs technique, a simple and fast computational method was proposed to fulfill converting high dimensional timeseries into a multiplex graph with different characters. The constructed multiplex graph inherits several properties of the time series in its structure. Thereby, periodic series, random series, and chaotic series convert into quite different multiplex networks with different average degree, characteristic path length, diameter, clustering coefficient, different degree distribution, and modularity, etc. By means of this new approach, with such different networks measures, one can characterize multivariate timeseries from a new viewpoint of complex networks.