LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection-Reference-Cited by-同舟云学术

LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection

Published:2024-05-13 Issue: Volume:8 Page:4138-4149
ISSN:
Container-title:Proceedings of the ACM Web Conference 2024
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Author:

Chen Feiyi¹^ORCID,Qin Zhen²^ORCID,Zhou Mengchu³^ORCID,Zhang Yingying⁴^ORCID,Deng Shuiguang²^ORCID,Fan Lunting⁴^ORCID,Pang Guansong⁵^ORCID,Wen Qingsong⁶^ORCID

Affiliation:

1. Zhejiang University & Alibaba Group, Hangzhou, China

2. Zhejiang University, Hangzhou, China

3. Zhejiang Gongshang University, Hangzhou, China

4. Alibaba Group, Hangzhou, China

5. Singapore Management University, Singapore, Singapore

6. Squirrel AI, Bellevue, WA, USA

Funder

National Science Foundation of China

Key Research Project of Zhejiang Province

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589334.3645472

Reference53 articles.

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3. Anomaly Detection under Distribution Shift

4. Hierarchical particle swarm optimization-incorporated latent factor analysis for large-scale incomplete matrices;Chen Jia;IEEE Transactions on Big Data,2021

5. Wenchao Chen, Long Tian, Bo Chen, Liang Dai, Zhibin Duan, and Mingyuan Zhou. 2022. Deep variational graph convolutional recurrent network for multivariate time series anomaly detection. In International Conference on Machine Learning. PMLR, 3621--3633.

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