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arXiv · 2502.14381

dtaianomaly: A Python library for time series anomaly detection

Louis Carpentier·Nick Seeuws·Wannes Meert·Mathias Verbeke
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Abstract

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.

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Louis Carpentier, Nick Seeuws, Wannes Meert, Mathias Verbeke. 2025-02-20. dtaianomaly: A Python library for time series anomaly detection. https://arxiv.org/abs/2502.14381

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