Construction of complex networks from time series based on the cross correlation interval

Abstract

AbstractIn this paper, a new approach to map time series into complex networks based on the cross correlation interval is proposed for the analysis of dynamic states of time series on different scales. In the proposed approach, a time series is divided into time series segments and each segment is reconstructed to a phase space defined as a node of the complex network. The cross correlation interval, which characterizes the degree of correlation between two phase spaces, is computed as the distance between the two nodes. The clustering coefficient and efficiency are used to determine an appropriate threshold for the construction of a complex network that can effectively describe the dynamic states of a complex system. In order to verify the efficiency of the proposed approach, complex networks are constructed for time series generated from the Lorenz system, for white Gaussian noise time series and for sea clutter time series. The experimental results have demonstrated that nodes in different communities represent different dynamic states . Therefore, the proposed approach can be used to uncover the dynamic characteristics of the complex systems.