dtaianomaly: A Python library for time series anomaly detection
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866929722261241856 |
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| author | Carpentier, Louis Seeuws, Nick Meert, Wannes Verbeke, Mathias |
| author_facet | Carpentier, Louis Seeuws, Nick Meert, Wannes Verbeke, Mathias |
| contents | dtaianomaly is an open-source Python library for time series anomaly detection, designed to bridge the gap between academic research and real-world applications. Our goal is to (1) accelerate the development of novel state-of-the-art anomaly detection techniques through simple extensibility; (2) offer functionality for large-scale experimental validation; and thereby (3) bring cutting-edge research to business and industry through a standardized API, similar to scikit-learn to lower the entry barrier for both new and experienced users. Besides these key features, dtaianomaly offers (1) a broad range of built-in anomaly detectors, (2) support for time series preprocessing, (3) tools for visual analysis, (4) confidence prediction of anomaly scores, (5) runtime and memory profiling, (6) comprehensive documentation, and (7) cross-platform unit testing.
The source code of dtaianomaly, documentation, code examples and installation guides are publicly available at https://github.com/ML-KULeuven/dtaianomaly. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14381 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | dtaianomaly: A Python library for time series anomaly detection Carpentier, Louis Seeuws, Nick Meert, Wannes Verbeke, Mathias Machine Learning Databases dtaianomaly is an open-source Python library for time series anomaly detection, designed to bridge the gap between academic research and real-world applications. Our goal is to (1) accelerate the development of novel state-of-the-art anomaly detection techniques through simple extensibility; (2) offer functionality for large-scale experimental validation; and thereby (3) bring cutting-edge research to business and industry through a standardized API, similar to scikit-learn to lower the entry barrier for both new and experienced users. Besides these key features, dtaianomaly offers (1) a broad range of built-in anomaly detectors, (2) support for time series preprocessing, (3) tools for visual analysis, (4) confidence prediction of anomaly scores, (5) runtime and memory profiling, (6) comprehensive documentation, and (7) cross-platform unit testing. The source code of dtaianomaly, documentation, code examples and installation guides are publicly available at https://github.com/ML-KULeuven/dtaianomaly. |
| title | dtaianomaly: A Python library for time series anomaly detection |
| topic | Machine Learning Databases |
| url | https://arxiv.org/abs/2502.14381 |