<scp>CutAddPaste:</scp> Time Series Anomaly Detection by Exploiting Abnormal Knowledge-Reference-Cited by-同舟云学术

CutAddPaste: Time Series Anomaly Detection by Exploiting Abnormal Knowledge

Published:2024-08-24 Issue: Volume:12 Page:3176-3187
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Wang Rui¹^ORCID,Mou Xudong¹^ORCID,Yang Renyu²^ORCID,Gao Kai³^ORCID,Liu Pin⁴^ORCID,Liu Chongwei⁵^ORCID,Wo Tianyu⁶^ORCID,Liu Xudong⁷^ORCID

Affiliation:

1. School of Computer Science and Engineering, Beihang University, Beijing, China

2. School of Software, Beihang University, Beijing, China

3. Institute of Future Cities, The Chinese University of Hong Kong, Hong Kong, China

4. School of Information Engineering, China University of Geosciences Beijing, Beijing, China

5. Kuaishou Inc., Beijing, China

6. School of Software, Beihang University & Zhongguancun Laboratory, Beijing, China

7. School of Computer Science and Engineering, Beihang University & Zhongguancun Laboratory, Beijing, China

Funder

National Key Research and Development Program of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671739

Reference35 articles.

1. Pankaj Malhotra, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal, and Gautam Shroff. Lstm-based encoder-decoder for multi-sensor anomaly detection. arXiv preprint arXiv:1607.00148, 2016.

2. Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, and Marius Kloft. Deep one-class classification. In International conference on machine learning, pages 4393--4402. PMLR, 2018.

3. Deep Contrastive One-Class Time Series Anomaly Detection

4. Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich. Deep anomaly detection with outlier exposure. In International Conference on Learning Representations, 2018.

5. CutPaste: Self-Supervised Learning for Anomaly Detection and Localization