Complex network-based time series analysis

Recent works show that complex network theory may be a powerful tool in time series analysis. We propose in this paper a reliable procedure for constructing complex networks from the correlation matrix of a time series. An original stock time series, the corresponding return series and its amplitude series are considered. The degree distribution of the original series can be well fitted with a power law, while that of the return series can be well fitted with a Gaussian function. The degree distribution of the amplitude series contains two asymmetric Gaussian branches. Reconstruction of networks from time series is a common problem in diverse research. The proposed strategy may be a reasonable solution to this problem.