Ruan Yijing, Donner Reik V, Guan Shuguang, Zou Yong
Department of Physics, East China Normal University, Shanghai 200062, China.
Department of Water, Environment, Construction and Safety, Magdeburg-Stendal University of Applied Sciences, Breitscheidstraße 2, 39114 Magdeburg, Germany.
Chaos. 2019 Apr;29(4):043111. doi: 10.1063/1.5086527.
It has been demonstrated that the construction of ordinal partition transition networks (OPTNs) from time series provides a prospective approach to improve our understanding of the underlying dynamical system. In this work, we introduce a suite of OPTN based complexity measures to infer the coupling direction between two dynamical systems from pairs of time series. For several examples of coupled stochastic processes, we demonstrate that our approach is able to successfully identify interaction delays of both unidirectional and bidirectional coupling configurations. Moreover, we show that the causal interaction between two coupled chaotic Hénon maps can be captured by the OPTN based complexity measures for a broad range of coupling strengths before the onset of synchronization. Finally, we apply our method to two real-world observational climate time series, disclosing the interaction delays underlying the temperature records from two distinct stations in Oxford and Vienna. Our results suggest that ordinal partition transition networks can be used as complementary tools for causal inference tasks and provide insights into the potentials and theoretical foundations of time series networks.
已经证明，从时间序列构建序数划分转移网络（OPTNs）为增进我们对潜在动力系统的理解提供了一种前瞻性方法。在这项工作中，我们引入了一套基于OPTN的复杂性度量，以从时间序列对中推断两个动力系统之间的耦合方向。对于几个耦合随机过程的例子，我们证明了我们的方法能够成功识别单向和双向耦合配置的相互作用延迟。此外，我们表明，在同步开始之前，基于OPTN的复杂性度量可以捕捉两个耦合混沌亨农映射之间的因果相互作用，适用于广泛的耦合强度范围。最后，我们将我们的方法应用于两个真实世界的观测气候时间序列，揭示了牛津和维也纳两个不同站点温度记录背后的相互作用延迟。我们的结果表明，序数划分转移网络可以用作因果推断任务的补充工具，并为时间序列网络的潜力和理论基础提供见解。
