Robust Anomaly Detection of Multivariate Time Series Data via Adversarial Graph Attention BiGRU
Multivariate time series data (MTSD) anomaly detection due to complex spatio-temporal dependencies among sensors and pervasive environmental noise. The existing methods struggle to balance anomaly detection accuracy with robustness against data contamination. Hence, this paper proposes a robust mult...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Big Data and Cognitive Computing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-2289/9/5/122 |
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| Summary: | Multivariate time series data (MTSD) anomaly detection due to complex spatio-temporal dependencies among sensors and pervasive environmental noise. The existing methods struggle to balance anomaly detection accuracy with robustness against data contamination. Hence, this paper proposes a robust multivariate temporal data anomaly detection method based on graph attention for training convolutional neural networks (PGAT-BiGRU-NRA). Firstly, the parallel graph attention (PGAT) mechanism extracts the time-dependent and spatially related features of MTSD to realize the MTSD fusion. Then, a bidirectional gate recurrent unit (BiGRU) is utilized to extract the contextual information of the data to avoid information loss. In addition, reconstructing the noise for adversarial training aims to achieve a more robust anomaly detection of MTSD. The experiments conducted on real industrial equipment datasets evaluate the effectiveness of the method in the task of MTSD, and the comparative experiments verify that the proposed method outperforms the mainstream baseline model. The proposed method achieves anomaly detection and robust performance in noise interference, which provides feasible technical support for the stable operation of industrial equipment in complex environments. |
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| ISSN: | 2504-2289 |