A Hybrid MTS Anomaly Detection Method Based on Reconstruction and Adaptive Spatial-Temporal Graph Network-Reference-Cited by-同舟云学术

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A Hybrid MTS Anomaly Detection Method Based on Reconstruction and Adaptive Spatial-Temporal Graph Network

Published:2024-05-17 Issue: Volume: Page:263-267
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Container-title:2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS)
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Author:

Yuan X.¹,Ning Ke¹,Wei K.²,He Yan-Lin¹,Zhu Qun-Xiong¹,Zhang Ming-Qing¹,Luo Yi³,Zhang Yang¹

Affiliation:

1. College of Information Science and Technology, Beijing University of Chemical Technology,Beijing,P. R. China,100020

2. Macao Polytechnic University,Faculty of Applied Sciences,Macao,SAR,P. R. China,999078

3. Research Institute of Mine Big Data, Chinese Institute of Coal Science,Beijing,P. R. China,100013

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10606543/10606546/10606793.pdf?arnumber=10606793

Reference16 articles.

1. Anomaly Detection in Quasi-Periodic Time Series based on Automatic Data Segmentation and Attentional LSTM-CNN

2. MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks

3. Graph Self-Supervised Learning: A Survey

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

5. Multivariate Time-Series Anomaly Detection via Graph Attention Network

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