| Full text | |
| Author(s): |
Total Authors: 4
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| Affiliation: | [1] East China Normal Univ, Dept Phys, Shanghai 200062 - Peoples R China
[2] Magdeburg Stendal Univ Appl Sci, Dept Water Environm Construct & Safety, Breitscheidstr 2, D-39114 Magdeburg - Germany
[3] Leibniz Soc, Potsdam Inst Climate Impact Res PIK, Telegrafenberg A31, D-14473 Potsdam - Germany
Total Affiliations: 3
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| Document type: | Journal article |
| Source: | Chaos; v. 29, n. 4 APR 2019. |
| Web of Science Citations: | 1 |
| Abstract | |
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 Henon 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. Published under license by AIP Publishing. (AU) | |
| FAPESP's process: | 11/50151-0 - Dynamical phenomena in complex networks: fundamentals and applications |
| Grantee: | Elbert Einstein Nehrer Macau |
| Support Opportunities: | Research Projects - Thematic Grants |