Python toolbox for stream anomaly (outlier) detection.
Project description
An anomaly detection package for streaming data.
Why StreamAD
Purpose & Advantages
StreamAD focuses on streaming settings, where data features evolve and distributions change over time. To prevent the failure of static models, StreamAD can correct its model as needed.
Incremental & Continual
StreamAD loads static datasets to a stream generator and feed a single observation at a time to any model in StreamAD. Therefore it can be used to simulate real-time applications and process streaming data.
Models & Algorithms
StreamAD collects open source implementations and reproduce state-of-the-art papers. Thus, it can also be used as an benchmark for academic.
Efficient & Scalability:
StreamAD concerns about the running time, resources usage and usability of different models. It is implemented by python and you can design your own algorithms and run with StreamAD.
Free & Open Source Software (FOSS)
StreamAD is distributed under BSD License 3.0 and favors FOSS principles.
Installation
The StreamAD framework can be installed via:
pip install -U StreamADAlternatively, you can install the library directly using the source code in Github repository by:
git clone https://github.com/Fengrui-Liu/StreamAD.git
cd StreamAD
pip install .Models
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