Department of Physics, East China Normal University, Shanghai, 200241, China.
School of Information Science Technology, East China Normal University, Shanghai, 200241, China.
Sci Rep. 2017 Aug 10;7(1):7795. doi: 10.1038/s41598-017-08245-x.
A growing number of algorithms have been proposed to map a scalar time series into ordinal partition transition networks. However, most observable phenomena in the empirical sciences are of a multivariate nature. We construct ordinal partition transition networks for multivariate time series. This approach yields weighted directed networks representing the pattern transition properties of time series in velocity space, which hence provides dynamic insights of the underling system. Furthermore, we propose a measure of entropy to characterize ordinal partition transition dynamics, which is sensitive to capturing the possible local geometric changes of phase space trajectories. We demonstrate the applicability of pattern transition networks to capture phase coherence to non-coherence transitions, and to characterize paths to phase synchronizations. Therefore, we conclude that the ordinal partition transition network approach provides complementary insight to the traditional symbolic analysis of nonlinear multivariate time series.
越来越多的算法被提出，以将标量时间序列映射到有序分区转移网络中。然而，经验科学中大多数可观察的现象都是多变量的性质。我们为多元时间序列构建有序分区转移网络。这种方法产生了加权有向网络，代表了速度空间中时间序列的模式转换特性，从而为基础系统提供了动态的见解。此外，我们提出了一种熵的度量来描述有序分区转移动力学，它能够敏感地捕捉到相空间轨迹可能的局部几何变化。我们展示了模式转换网络在捕获相位相干到非相干转换以及描述相位同步路径方面的适用性。因此，我们得出结论，有序分区转移网络方法为传统的非线性多元时间序列的符号分析提供了补充的见解。
