PySAD
- Android app
- Not listed
- Free plan
- Yes
- Runs on
- Linux, Mac, Windows
Summary
PySAD is a free, open-source Python framework for detecting anomalies in streaming data. Its online models update as each new instance arrives, supporting sequential detection on both univariate and multivariate data. The project describes 16 online detectors, including xStream, LODA, RS-Hash, Half-Space Trees, and Robust Random Cut Forest. PySAD includes stream simulators, evaluators, preprocessors, statistic trackers, postprocessors, and probability calibrators for building and assessing detection workflows. It also connects batch anomaly detectors from PyOD to streaming use. Experiments can use supervised, semi-supervised, or unsupervised settings. The documentation notes that streaming methods may retain a single instance or a small recent window to meet memory and processing constraints. PySAD can be installed with pip or from its GitHub source, and its current README lists Python 3.10 or newer on Linux, macOS, and Windows. It is self-hosted and distributed under a BSD 3-Clause license. The project lists 17 labelled benchmark datasets that download on first use.
Who it is for
PySAD suits Python users building or evaluating anomaly detection for streaming univariate or multivariate data. It also fits those experimenting with supervised, semi-supervised, and unsupervised approaches.
What is good
- Includes 16 online detectors.
- Supports univariate and multivariate streaming data.
- Offers tools for evaluating and preprocessing streams.
- Free and open source under BSD 3-Clause.
What to know first
- Requires Python 3.10 or newer.
- Self-hosted deployment is listed.
- 17 benchmark datasets download on first use.
Verdict
PySAD provides online detectors and supporting evaluation tools in a free Python framework. Its streaming focus and listed Python requirement make it most relevant to users comfortable with that environment.
PySAD plans and pricing
All plansCompared on anomaly detection software
- Free plan
- Yesgithub.com
- Detection method
- hybridgithub.com
- Real-time detection
- Yesgithub.com
- Supported data
- univariate data; multivariate data; streaming datagithub.com
- Deployment options
- self-hostedgithub.com
Facts
- Purpose
- PySAD is an open-source Python framework for anomaly detection on streaming multivariate data.pysad.readthedocs.io · 2 Oct 2026
- Online detection
- Its models update as each new data instance arrives for online or sequential anomaly detection.pysad.readthedocs.io · 2 Oct 2026
- Detectors
- The repository describes 16 online detectors, including xStream, LODA, RS-Hash, Half-Space Trees, and Robust Random Cut Forest.github.com · 2 Oct 2026
- Resource use
- The documentation says streaming methods may store only an instance or a small window of recent instances to meet memory and processing constraints.pysad.readthedocs.io · 2 Oct 2026
- Evaluation tools
- PySAD includes stream simulators, evaluators, preprocessors, statistic trackers, postprocessors, and probability calibrators.pysad.readthedocs.io · 2 Oct 2026
- PyOD integration
- PySAD provides integrations that let batch anomaly detectors from PyOD run in a streaming setting.pysad.readthedocs.io · 2 Oct 2026
- Data and learning settings
- The framework supports models for univariate and multivariate data and experiments in supervised, semi-supervised, and unsupervised settings.pysad.readthedocs.io · 2 Oct 2026
- Installation
- The project can be installed with pip or from its GitHub source, and its current README lists Python 3.10 or newer on Linux, macOS, and Windows.github.com · 2 Oct 2026
- License
- The project is distributed under a BSD 3-Clause license.github.com · 2 Oct 2026
- Support
- The maintainer says issues and pull requests are welcome and aims to reply within a few days; questions and show-and-tell go in GitHub Discussions.github.com · 2 Oct 2026
- Benchmarks
- The README says 17 labelled benchmark datasets are downloaded on first use.github.com · 2 Oct 2026
- Security
- The repository links to a security policy, but the policy page could not be opened during this research.github.com · 2 Oct 2026
- Maintainer
- The GitHub profile identifies Selim Firat Yilmaz as an AI researcher based in London, UK.github.com · 2 Oct 2026
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Sources
- pysad.readthedocs.io/en/stable/· checked 2 Oct 2026
- github.com/selimfirat/pysad· checked 2 Oct 2026
- github.com/selimfirat· checked 2 Oct 2026