An Empirical Analysis of Anomaly Detection Methods for Multivariate Time Series-Reference-Cited by-同舟云学术

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An Empirical Analysis of Anomaly Detection Methods for Multivariate Time Series

Published:2023-10-09 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE)
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Author:

Li Dongwen¹,Zhang Shenglin¹,Sun Yongqian¹,Guo Yang¹,Che Zeyu¹,Chen Shiqi¹,Zhong Zhenyu¹,Liang Minghan¹,Shao Minyi¹,Li Mingjie¹,Liu Shuyang¹,Zhang Yuzhi¹,Pei Dan²

Affiliation:

1. Nankai University

2. Tsinghua University

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10299935/10299936/10301273.pdf?arnumber=10301273

Reference27 articles.

1. Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network

2. USAD

3. Graph Neural Network-Based Anomaly Detection in Multivariate Time Series

4. SDFVAE: Static and Dynamic Factorized VAE for Anomaly Detection of Multivariate CDN KPIs

5. Deep Multi-Instance Contrastive Learning with Dual Attention for Anomaly Precursor Detection

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