dtaianomaly: A Python library for time series anomaly detection

TL;DR

dtaianomaly is a comprehensive open-source Python library that facilitates time series anomaly detection, combining research, validation, and practical application features with an easy-to-use API.

Contribution

It introduces a standardized, extensible framework for time series anomaly detection that bridges academic research and industry needs.

Findings

Includes a broad range of anomaly detection algorithms

Supports large-scale experimental validation

Provides tools for visualization and confidence prediction

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.