Network analysis of time series under the constraint of fixed nearest neighbors

Abstract

In this paper, we carried out network analysis for typical time series, such as periodic signals, chaotic maps, Gaussian white noise, and fractal Brownian motions. By reconstructing the phase space for a given time series, we can generate a network under the constraint of fixed nearest neighbors. The mapped networks are then analyzed from both the statistical properties, such as degree distribution, clustering coefficient, betweenness, etc, as well as the local topological structures, i.e., network motifs. It is shown that time series of different nature can be distinguished from these two aspects of the constructed networks.