Work

Robust Anomaly Detection of Multivariate Time Series Data via Adversarial Graph Attention BiGRU

Year: 2025

Type: article

Abstract: 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.

Source: Big Data and Cognitive Computing

DOI: 10.3390/bdcc9050122

Authors: Yajing Xing, Jinbiao Tan, Rui Zhang, Jiafu Wan

Institutions: Guangdong Key Laboratory of Precision Equipment and Manufacturing Technology, South China University of Technology

Language: English

Topic: Anomaly Detection Techniques and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,172

Funder: Guangzhou Municipal Science and Technology Project