Journal of Computer Reasearch and Development - Anomaly Detection
This is the source code of the paper named "Fusion Learning Based Unsupervised Anomaly Detection for Multi-Dimensional Time Series" and published in Journal of Computer Reasearch and Development.
Please cite our paper if you find this code is useful.
Zhou Xiaohui, Wang Yijie, Xu Hongzuo, Liu Mingyu. Fusion Learning Based Unsupervised Anomaly Detection for Multi-Dimensional Time Series[J]. Journal of Computer Research and Development, 2023, 60(3): 496-508. doi: 10.7544/issn1000-1239.202220490.
- run main.py for sample usage.
- Data set: You may want to find the sample input data set in the "datasets" folder.
- The input path can be an individual data set or just a folder.
- The performance might have slight differences between two independent runs. In our paper, we report the average auc with std over 5 runs.
Python 3.6
Troch == 1.7.0+cu110
pandas == 1.1.5
scikit-learn == 1.0.1
numpy == 1.21.6