Self-attention-based graph transformation learning for anomaly detection in multivariate time series
Abstract Multivariate time series anomaly detection has widely applications in many fields such as finance, power, and industry. Recently, Graph Neural Network (GNN) have achieved great success in this task due to their powerful ability of modeling multivariate relationships. However, most existing...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-03-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-025-01839-3 |
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