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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.

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.

StreamAD collects open source implementations and reproduce state-of-the-art papers. Thus, it can also be used as an benchmark for academic.

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.

StreamAD is distributed under BSD License 3.0 and favors FOSS principles.

The StreamAD framework can be installed via:

pip install -U streamad

Alternatively, 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 .

Semantic versioning is used for this project.

• KNN CAD
• xStream
• SPOT
• LSTMAutoencoder