Hypergraphs on high dimensional time series sets using signature transform
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916854165929984 |
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| author | Vaucher, Rémi Minchella, Paul |
| author_facet | Vaucher, Rémi Minchella, Paul |
| contents | In recent decades, hypergraphs and their analysis through Topological Data Analysis (TDA) have emerged as powerful tools for understanding complex data structures. Various methods have been developed to construct hypergraphs -- referred to as simplicial complexes in the TDA framework -- over datasets, enabling the formation of edges between more than two vertices. This paper addresses the challenge of constructing hypergraphs from collections of multivariate time series. While prior work has focused on the case of a single multivariate time series, we extend this framework to handle collections of such time series. Our approach generalizes the method proposed in Chretien and al. by leveraging the properties of signature transforms to introduce controlled randomness, thereby enhancing the robustness of the construction process. We validate our method on synthetic datasets and present promising results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15802 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hypergraphs on high dimensional time series sets using signature transform Vaucher, Rémi Minchella, Paul Machine Learning Computation In recent decades, hypergraphs and their analysis through Topological Data Analysis (TDA) have emerged as powerful tools for understanding complex data structures. Various methods have been developed to construct hypergraphs -- referred to as simplicial complexes in the TDA framework -- over datasets, enabling the formation of edges between more than two vertices. This paper addresses the challenge of constructing hypergraphs from collections of multivariate time series. While prior work has focused on the case of a single multivariate time series, we extend this framework to handle collections of such time series. Our approach generalizes the method proposed in Chretien and al. by leveraging the properties of signature transforms to introduce controlled randomness, thereby enhancing the robustness of the construction process. We validate our method on synthetic datasets and present promising results. |
| title | Hypergraphs on high dimensional time series sets using signature transform |
| topic | Machine Learning Computation |
| url | https://arxiv.org/abs/2507.15802 |