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App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 2

Anomaly detection for multivariate time series based on dual-graph and multi-level contrastive learning

Wu Yulu
Ling Jie
School of Computer Science & Technology, Guangdong University of Technology, Guangzhou 510006, China
DOI: 10.19734/j.issn.1001-3695.2025.07.0236

Abstract

Multivariate time series anomaly detection is crucial for ensuring the stable operation of industrial systems and the Internet of Things. Existing methods often inadequately capture complex inter-variable dependencies in multivariate time series and lack sufficient capability to model normal patterns, resulting in suboptimal anomaly discrimination. To address these problems, this paper proposed DGMLC, a dual-graph and multi-level contrastive learning approach for multivariate time series anomaly detection. The method constructed temporal and spatial graphs using graph attention mechanisms to comprehensively extract time-series temporal features and variable dependencies. It then aggregated these graphs via convolutional operations to generate a feature-enhanced graph with attention weights. A multi-level contrastive learning strategy jointly optimized the temporal graph, spatial graph, and feature-enhanced graph to predict normal patterns, while the model identified anomalies based on prediction errors. Experimental results on SMAP, MSL, SWaT, and WADI datasets demonstrate that the proposed method achieves an average F1-score of 92.86%, showing significant improvement over existing methods. Results demonstrate its superior capability in capturing variable dependencies and modeling normal patterns, confirming its effectiveness and promising application potential in multivariate time series anomaly detection.

Foundation Support

广州市重点领域研发计划资助项目(202007010004)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.07.0236
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 2

Publish History

[2025-10-26] Accepted Paper

Cite This Article

吴雨露, 凌捷. 基于双图与多层次对比的多变量时间序列异常检测方法 [J]. 计算机应用研究, 2026, 43 (2). (2025-11-04). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0236. (Wu Yulu, Ling Jie. Anomaly detection for multivariate time series based on dual-graph and multi-level contrastive learning [J]. Application Research of Computers, 2026, 43 (2). (2025-11-04). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0236. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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