Bivariate partial mapping for detecting causality in complex non-autonomous system
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915878062260224 |
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| author | Ni, Yang Liu, Changqing Zhang, Yifan Gao, Yifan Guo, Haonan Gao, James Li, Yingguang |
| author_facet | Ni, Yang Liu, Changqing Zhang, Yifan Gao, Yifan Guo, Haonan Gao, James Li, Yingguang |
| contents | Identifying causality is fundamental for human understanding of the world, where complex non-autonomous systems such as species population changes, brain activities, etc. are extensively existed. Since the phase spaces of such systems are not manifolds, the existing method based on convergent cross mapping is not applicable. This paper proposes a novel bivariate partial mapping method for detecting causality in complex non-autonomous systems. It transforms a non-autonomous system to an autonomous skew product system, and then, by considering the causality changes due to the transformation, detects causality of the original non-autonomous system from the transformed skew product system. The effectiveness of the proposed method is verified by mathematical cases and a real brain activity case, showing that the proposed method successfully detects the causality in complex non-autonomous systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_20331 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Bivariate partial mapping for detecting causality in complex non-autonomous system Ni, Yang Liu, Changqing Zhang, Yifan Gao, Yifan Guo, Haonan Gao, James Li, Yingguang Dynamical Systems Identifying causality is fundamental for human understanding of the world, where complex non-autonomous systems such as species population changes, brain activities, etc. are extensively existed. Since the phase spaces of such systems are not manifolds, the existing method based on convergent cross mapping is not applicable. This paper proposes a novel bivariate partial mapping method for detecting causality in complex non-autonomous systems. It transforms a non-autonomous system to an autonomous skew product system, and then, by considering the causality changes due to the transformation, detects causality of the original non-autonomous system from the transformed skew product system. The effectiveness of the proposed method is verified by mathematical cases and a real brain activity case, showing that the proposed method successfully detects the causality in complex non-autonomous systems. |
| title | Bivariate partial mapping for detecting causality in complex non-autonomous system |
| topic | Dynamical Systems |
| url | https://arxiv.org/abs/2603.20331 |