PySAD: A Streaming Anomaly Detection Framework in Python

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 atthis http URL.

@article{yilmaz2025_2009.02572,
  title={ PySAD: A Streaming Anomaly Detection Framework in Python },
  author={ Selim F. Yilmaz and Suleyman S. Kozat },
  journal={arXiv preprint arXiv:2009.02572},
  year={ 2025 }
}