Topological Analysis for Detecting Anomalies (TADA) in Time Series
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866929380333191168 |
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| author | Chazal, Frédéric Royer, Martin Levrard, Clément |
| author_facet | Chazal, Frédéric Royer, Martin Levrard, Clément |
| contents | This paper introduces new methodology based on the field of Topological Data Analysis for detecting anomalies in multivariate time series, that aims to detect global changes in the dependency structure between channels. The proposed approach is lean enough to handle large scale datasets, and extensive numerical experiments back the intuition that it is more suitable for detecting global changes of correlation structures than existing methods. Some theoretical guarantees for quantization algorithms based on dependent time sequences are also provided. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06168 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Topological Analysis for Detecting Anomalies (TADA) in Time Series Chazal, Frédéric Royer, Martin Levrard, Clément Statistics Theory Machine Learning This paper introduces new methodology based on the field of Topological Data Analysis for detecting anomalies in multivariate time series, that aims to detect global changes in the dependency structure between channels. The proposed approach is lean enough to handle large scale datasets, and extensive numerical experiments back the intuition that it is more suitable for detecting global changes of correlation structures than existing methods. Some theoretical guarantees for quantization algorithms based on dependent time sequences are also provided. |
| title | Topological Analysis for Detecting Anomalies (TADA) in Time Series |
| topic | Statistics Theory Machine Learning |
| url | https://arxiv.org/abs/2406.06168 |