Anomaly detection by partitioning of multi-variate time series
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911184001695744 |
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| author | Lotte, Pierre Péninou, André Teste, Olivier |
| author_facet | Lotte, Pierre Péninou, André Teste, Olivier |
| contents | In this article, we suggest a novel non-supervised partition based anomaly detection method for anomaly detection in multivariate time series called PARADISE. This methodology creates a partition of the variables of the time series while ensuring that the inter-variable relations remain untouched. This partitioning relies on the clustering of multiple correlation coefficients between variables to identify subsets of variables before executing anomaly detection algorithms locally for each of those subsets. Through multiple experimentations done on both synthetic and real datasets coming from the literature, we show the relevance of our approach with a significant improvement in anomaly detection performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25215 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Anomaly detection by partitioning of multi-variate time series Lotte, Pierre Péninou, André Teste, Olivier Machine Learning Signal Processing In this article, we suggest a novel non-supervised partition based anomaly detection method for anomaly detection in multivariate time series called PARADISE. This methodology creates a partition of the variables of the time series while ensuring that the inter-variable relations remain untouched. This partitioning relies on the clustering of multiple correlation coefficients between variables to identify subsets of variables before executing anomaly detection algorithms locally for each of those subsets. Through multiple experimentations done on both synthetic and real datasets coming from the literature, we show the relevance of our approach with a significant improvement in anomaly detection performance. |
| title | Anomaly detection by partitioning of multi-variate time series |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2509.25215 |