Causal Discovery in Symmetric Dynamic Systems with Convergent Cross Mapping
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915277513424896 |
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| author | Duan, Yiting Guo, Yi Yang, Jack Yin, Ming |
| author_facet | Duan, Yiting Guo, Yi Yang, Jack Yin, Ming |
| contents | This paper systematically discusses how the inherent properties of chaotic attractors influence the results of discovering causality from time series using convergent cross mapping, particularly how convergent cross mapping misleads bidirectional causality as unidirectional when the chaotic attractor exhibits symmetry. We propose a novel method based on the k-means clustering method to address the challenges when the chaotic attractor exhibits two-fold rotation symmetry. This method is demonstrated to recover the symmetry of the latent chaotic attractor and discover the correct causality between time series without introducing information from other variables. We validate the accuracy of this method using time series derived from low-dimension and high-dimensional chaotic symmetric attractors for which convergent cross mapping may conclude erroneous results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_04815 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Causal Discovery in Symmetric Dynamic Systems with Convergent Cross Mapping Duan, Yiting Guo, Yi Yang, Jack Yin, Ming Dynamical Systems This paper systematically discusses how the inherent properties of chaotic attractors influence the results of discovering causality from time series using convergent cross mapping, particularly how convergent cross mapping misleads bidirectional causality as unidirectional when the chaotic attractor exhibits symmetry. We propose a novel method based on the k-means clustering method to address the challenges when the chaotic attractor exhibits two-fold rotation symmetry. This method is demonstrated to recover the symmetry of the latent chaotic attractor and discover the correct causality between time series without introducing information from other variables. We validate the accuracy of this method using time series derived from low-dimension and high-dimensional chaotic symmetric attractors for which convergent cross mapping may conclude erroneous results. |
| title | Causal Discovery in Symmetric Dynamic Systems with Convergent Cross Mapping |
| topic | Dynamical Systems |
| url | https://arxiv.org/abs/2505.04815 |