Welcome to DeepOD documentation!

DeepOD is an open-source python library for Deep Learning-based Outlier Detection and Anomaly Detection. DeepOD supports tabular anomaly detection and time-series anomaly detection.

DeepOD includes 27 deep outlier detection / anomaly detection algorithms (in unsupervised/weakly-supervised paradigm). More baseline algorithms will be included later.

DeepOD is featured for:

Unified APIs across various algorithms.
SOTA models includes reconstruction-, representation-learning-, and self-superivsed-based latest deep learning methods.
Comprehensive Testbed that can be used to directly test different models on benchmark datasets (highly recommend for academic research).
Versatile in different data types including tabular and time-series data (DeepOD will support other data types like images, graph, log, trace, etc. in the future, welcome PR :telescope:).
Diverse Network Structures can be plugged into detection models, we now support LSTM, GRU, TCN, Conv, and Transformer for time-series data. (welcome PR as well :sparkles:)

If you are interested in our project, we are pleased to have your stars and forks :thumbsup: :beers: .

Citation

If you use this library in your work, please cite this paper:

Hongzuo Xu, Guansong Pang, Yijie Wang and Yongjun Wang, “Deep Isolation Forest for Anomaly Detection,” in IEEE Transactions on Knowledge and Data Engineering, doi: 10.1109/TKDE.2023.3270293.

You can also use the BibTex entry below for citation.

@ARTICLE{xu2023deep,
   author={Xu, Hongzuo and Pang, Guansong and Wang, Yijie and Wang, Yongjun},
   journal={IEEE Transactions on Knowledge and Data Engineering},
   title={Deep Isolation Forest for Anomaly Detection},
   year={2023},
   volume={},
   number={},
   pages={1-14},
   doi={10.1109/TKDE.2023.3270293}
}