From time series to complex networks: The phase space coarse graining

In this paper, we present a simple and fast computational method, the phase space coarse graining algorithm that converts a time series into a directed and weighted complex network. The constructed directed and weighted complex network inherits several properties of the series in its structure. Thereby, periodic series convert into regular networks, and random series do so into random networks. Moreover, chaotic series convert into scale-free networks. It is shown that the phase space coarse graining algorithm allows us to distinguish, identify and describe in detail various time series. Finally, we apply the phase space coarse graining algorithm to the practical observations series, international gasoline regular spot price series and identify its dynamic characteristics.