Topology-driven identification of repetitions in multi-variate time series
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866918025071951872 |
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| author | Schindler, Simon Reich, Elias Steffen Messineo, Saverio Hoher, Simon Huber, Stefan |
| author_facet | Schindler, Simon Reich, Elias Steffen Messineo, Saverio Hoher, Simon Huber, Stefan |
| contents | Many multi-variate time series obtained in the natural sciences and engineering possess a repetitive behavior, as for instance state-space trajectories of industrial machines in discrete automation. Recovering the times of recurrence from such a multi-variate time series is of a fundamental importance for many monitoring and control tasks. For a periodic time series this is equivalent to determining its period length. In this work we present a persistent homology framework to estimate recurrence times in multi-variate time series with different generalizations of cyclic behavior (periodic, repetitive, and recurring). To this end, we provide three specialized methods within our framework that are provably stable and validate them using real-world data, including a new benchmark dataset from an injection molding machine. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10004 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Topology-driven identification of repetitions in multi-variate time series Schindler, Simon Reich, Elias Steffen Messineo, Saverio Hoher, Simon Huber, Stefan Computational Geometry Signal Processing Algebraic Topology Machine Learning Many multi-variate time series obtained in the natural sciences and engineering possess a repetitive behavior, as for instance state-space trajectories of industrial machines in discrete automation. Recovering the times of recurrence from such a multi-variate time series is of a fundamental importance for many monitoring and control tasks. For a periodic time series this is equivalent to determining its period length. In this work we present a persistent homology framework to estimate recurrence times in multi-variate time series with different generalizations of cyclic behavior (periodic, repetitive, and recurring). To this end, we provide three specialized methods within our framework that are provably stable and validate them using real-world data, including a new benchmark dataset from an injection molding machine. |
| title | Topology-driven identification of repetitions in multi-variate time series |
| topic | Computational Geometry Signal Processing Algebraic Topology Machine Learning |
| url | https://arxiv.org/abs/2505.10004 |