PySAD: A Streaming Anomaly Detection Framework in Python
This provides a comprehensive toolkit for researchers and practitioners working with real-time anomaly detection in streaming data, though it is incremental as it builds on existing algorithms within a new framework.
The authors tackled the problem of streaming anomaly detection under strict constraints like bounded memory and single-pass processing by presenting PySAD, a Python framework that implements 17+ algorithms and achieves efficient real-time processing while maintaining compatibility with existing tools.
Streaming anomaly detection requires algorithms that operate under strict constraints: bounded memory, single-pass processing, and constant-time complexity. We present PySAD, a comprehensive Python framework addressing these challenges through a unified architecture. The framework implements 17+ streaming algorithms (LODA, Half-Space Trees, xStream) with specialized components including projectors, probability calibrators, and postprocessors. Unlike existing batch-focused frameworks, PySAD enables efficient real-time processing with bounded memory while maintaining compatibility with PyOD and scikit-learn. Supporting all learning paradigms for univariate and multivariate streams, PySAD provides the most comprehensive streaming anomaly detection toolkit in Python. The source code is publicly available at github.com/selimfirat/pysad.