Multiscale complex network constructed from chaotic time series

Complex networks play a significant role in modern complex systems sciences in that they allow for the quantitative analysis of the structural properties of systems composed of different interacting entities. Recently, intensive efforts have been made to apply network-based concepts also for the analysis of time series. In this reported work, multiscale complex networks from the chaotic time series are constructed by a decomposition strategy. Since different components exist in the time series, it was found that the constructed network inherits the multiscale properties of the time series in its structure. For example, periodic series and noisy series convert into regular networks and random networks, respectively. A novel method for the chaotic time series analysis is provided.