{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T22:18:56Z","timestamp":1782944336326,"version":"3.54.5"},"reference-count":38,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T00:00:00Z","timestamp":1746662400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Special Project on Cooperation and Exchange of Shanxi Province Science and Technology","award":["202204041101036"],"award-info":[{"award-number":["202204041101036"]}]},{"name":"Special Project on Cooperation and Exchange of Shanxi Province Science and Technology","award":["2024B03J1297"],"award-info":[{"award-number":["2024B03J1297"]}]},{"name":"Guangzhou Science and Technology Plan Project","award":["202204041101036"],"award-info":[{"award-number":["202204041101036"]}]},{"name":"Guangzhou Science and Technology Plan Project","award":["2024B03J1297"],"award-info":[{"award-number":["2024B03J1297"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/bdcc9050122","type":"journal-article","created":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T06:47:15Z","timestamp":1746686835000},"page":"122","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Robust Anomaly Detection of Multivariate Time Series Data via Adversarial Graph Attention BiGRU"],"prefix":"10.3390","volume":"9","author":[{"given":"Yajing","family":"Xing","sequence":"first","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Precision Equipment and Manufacturing Technology, School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinbiao","family":"Tan","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Precision Equipment and Manufacturing Technology, School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanxi Information Industry Technology Research Institute Company Ltd., Taiyuan 030032, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9188-4179","authenticated-orcid":false,"given":"Jiafu","family":"Wan","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Precision Equipment and Manufacturing Technology, School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mutawa, A.M. 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