dtaianomaly: A Python library for time series anomaly detection
This library addresses the gap between research and industry in time series anomaly detection, though it is incremental as it builds on existing paradigms like scikit-learn.
The authors developed dtaianomaly, an open-source Python library for time series anomaly detection that aims to bridge academic research and real-world applications by providing extensibility for novel methods and tools for large-scale validation, with features including built-in detectors, preprocessing, visualization, and a scikit-learn-like API.
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.