FluxEV: A Fast and Effective Unsupervised Framework for Time-Series Anomaly Detection-Reference-Cited by-同舟云学术

FluxEV: A Fast and Effective Unsupervised Framework for Time-Series Anomaly Detection

Published:2021-03-08 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 14th ACM International Conference on Web Search and Data Mining
language:
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Author:

Li Jia¹,Di Shimin¹,Shen Yanyan²,Chen Lei¹

Affiliation:

1. The Hong Kong University of Science and Technology, Hong Kong, China

2. Shanghai Jiao Tong University, Shanghai, China

Funder

Hong Kong RGC AOE Project

Hong Kong ITC ITF grants

China NSFC

Didi-HKUST joint research lab project

Hong Kong RGC CRF Project

Microsoft Research Asia Collaborative Research Grant

Hong Kong RGC GRF Project

Wechat and Webank Research Grants

Guangdong Basic and Applied Basic Research Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3437963.3441823

Reference37 articles.

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4. [n.d.]. https://github.com/DawnsonLi/EVT. [n.d.]. https://github.com/DawnsonLi/EVT.

5. [n.d.]. AIOps Challenge Final Dataset. http://iops.ai/dataset_detail/?id=10. [n.d.]. AIOps Challenge Final Dataset. http://iops.ai/dataset_detail/?id=10.

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