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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2406.17698 |
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| _version_ | 1866914848794738688 |
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| author | Balsells-Rodas, Carles Wang, Yixin Mediano, Pedro A. M. Li, Yingzhen |
| author_facet | Balsells-Rodas, Carles Wang, Yixin Mediano, Pedro A. M. Li, Yingzhen |
| contents | Causal discovery in time series is a rapidly evolving field with a wide variety of applications in other areas such as climate science and neuroscience. Traditional approaches assume a stationary causal graph, which can be adapted to nonstationary time series with time-dependent effects or heterogeneous noise. In this work we address nonstationarity via regime-dependent causal structures. We first establish identifiability for high-order Markov Switching Models, which provide the foundations for identifiable regime-dependent causal discovery. Our empirical studies demonstrate the scalability of our proposed approach for high-order regime-dependent structure estimation, and we illustrate its applicability on brain activity data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_17698 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Identifying Nonstationary Causal Structures with High-Order Markov Switching Models Balsells-Rodas, Carles Wang, Yixin Mediano, Pedro A. M. Li, Yingzhen Machine Learning Causal discovery in time series is a rapidly evolving field with a wide variety of applications in other areas such as climate science and neuroscience. Traditional approaches assume a stationary causal graph, which can be adapted to nonstationary time series with time-dependent effects or heterogeneous noise. In this work we address nonstationarity via regime-dependent causal structures. We first establish identifiability for high-order Markov Switching Models, which provide the foundations for identifiable regime-dependent causal discovery. Our empirical studies demonstrate the scalability of our proposed approach for high-order regime-dependent structure estimation, and we illustrate its applicability on brain activity data. |
| title | Identifying Nonstationary Causal Structures with High-Order Markov Switching Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2406.17698 |