Sequential Anomaly Detection with Local and Global Explanations
Abstract
We propose GLEAD, a framework for sequential anomaly detection that provides both local explanations (highlighting anomalous subsequences) and global explanations (dataset-wide patterns). This dual perspective improves transparency for practitioners while maintaining strong detection performance.
Type
Publication
In IEEE International Conference on Big Data (Big Data 2022)
Citation
@inproceedings{cheng2022sequential,
title={Sequential anomaly detection with local and global explanations},
author={Cheng, He and Xu, Depeng and Yuan, Shuhan},
booktitle={2022 IEEE International Conference on Big Data (Big Data)},
pages={1212--1217},
year={2022},
organization={IEEE}
}
