Correlation-Aware Spatial–Temporal Graph Learning for Multivariate Time-Series Anomaly Detection

Author:

Zheng Yu1ORCID,Koh Huan Yee2ORCID,Jin Ming2ORCID,Chi Lianhua1ORCID,Phan Khoa T.1ORCID,Pan Shirui3ORCID,Chen Yi-Ping Phoebe1ORCID,Xiang Wei1ORCID

Affiliation:

1. Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, Australia

2. Department of Data Science and AI, Faculty of IT, Monash University, Clayton, VIC, Australia

3. School of Information and Communication Technology, Griffith University, Southport, QLD, Australia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Multitask Dynamic Graph Attention Autoencoder for Imbalanced Multilabel Time Series Classification;IEEE Transactions on Neural Networks and Learning Systems;2024-09

2. DyEdgeGAT: Dynamic Edge via Graph Attention for Early Fault Detection in IIoT Systems;IEEE Internet of Things Journal;2024-07-01

3. Graph spatiotemporal process for multivariate time series anomaly detection with missing values;Information Fusion;2024-06

4. A Novel Data Mining Framework for Discovering Association Rules in Multivariate Time Series;2024 7th International Conference on Advanced Algorithms and Control Engineering (ICAACE);2024-03-01

5. AD-NEv: A Scalable Multilevel Neuroevolution Framework for Multivariate Anomaly Detection;IEEE Transactions on Neural Networks and Learning Systems;2024

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