2025/02/20 by Louis Carpentier, Nick Seeuws, Carpentier, Louis +5
Computer Science · #Anomaly Detection Techniques and Applications #Computational Physics and Python Applications #Databases (cs.DB) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Time Series Analysis and Forecasting
paper · doi:10.48550/arxiv.2502.14381
openalex publication_date 2025/02/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.