# PySAD: A Streaming Anomaly Detection Framework in Python

arXiv: https://arxiv.org/abs/2009.02572 · HTML: https://mlsanity.com/paper/2009.02572/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2020.

## Built on by (most cited first, 9 of 9)

- [MemStream: Memory-Based Streaming Anomaly Detection](https://mlsanity.com/paper/2106.03837/) (2021)
- [METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection](https://mlsanity.com/paper/2312.16831/) (2023)
- [Outlier Detection using AI: A Survey](https://mlsanity.com/paper/2112.00588/) (2021)
- [Multi-Aspect Mining and Anomaly Detection for Heterogeneous Tensor Streams](https://mlsanity.com/paper/2602.04917/) (2026)
- [A Review of Open Source Software Tools for Time Series Analysis](https://mlsanity.com/paper/2203.05195/) (2022)
- [Incremental Outlier Detection Modelling Using Streaming Analytics in Finance & Health Care](https://mlsanity.com/paper/2305.09907/) (2023)
- [fSEAD: a Composable FPGA-based Streaming Ensemble Anomaly Detection Library](https://mlsanity.com/paper/2406.05999/) (2024)
- [Unsupervised anomaly detection on cybersecurity data streams: a case with BETH dataset](https://mlsanity.com/paper/2503.04178/) (2025)
- [Online Isolation Forest](https://mlsanity.com/paper/2505.09593/) (2025)

## Abstract (arXiv)

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.