dtaianomaly: A Python library for time series anomaly detection

2025-02-20

AI Summary

Time series anomaly detection suffers from a persistent gap between academic research and industrial deployment. To bridge this gap, we propose and open-source TSAD (Time Series Anomaly Detection), a standardized Python library designed for end-to-end workflows—including algorithm development, large-scale experimental validation, and production deployment. Its key contributions are: (1) a novel extensible architecture integrating confidence estimation, resource profiling, and multidimensional visualization; (2) a scikit-learn–style API that significantly lowers adoption barriers for researchers and practitioners; and (3) comprehensive support for dozens of anomaly detectors and preprocessing techniques, accompanied by full documentation, benchmark suites, and CI/CD pipelines. Hosted on GitHub, TSAD has been adopted by multiple academic groups and industrial organizations, effectively closing the research–validation–application loop in time series anomaly detection.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Multiagent Systems: Adversarial Agents

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Develop Python library for time series anomaly detection
• Bridge academic research and real-world applications
• Lower entry barrier with standardized API

Innovation

• Open-source Python library for anomaly detection
• Extensible API for novel detection techniques
• Supports large-scale experimental validation