PySAD: A Streaming Anomaly Detection Framework in Python

• URL: http://arxiv.org/abs/2009.02572v2
• Date: Sun, 25 May 2025 00:23:23 GMT
• Title: PySAD: A Streaming Anomaly Detection Framework in Python
• Authors: Selim F. Yilmaz, Suleyman S. Kozat,
• Abstract summary: Streaming anomaly detection requires algorithms that operate under strict constraints.<n>We present PySAD, a comprehensive Python framework addressing these challenges through a unified architecture.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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