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System Development & Application
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App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 2
507-513

Integrating contrastive learning dual-branch multivariate time series anomaly detection method

Zhou Dan
Ling Jie
School of Computer Science & Technology, Guangdong University of Technology, Guangzhou 510006, China
DOI: 10.19734/j.issn.1001-3695.2024.07.0286

Abstract

Multivariate time series anomaly detection is essential for maintaining the effective operation of complex industrial systems. Accurately identifying anomalous patterns across numerous devices presents a significant challenge. To address this challenge, this paper proposed a dual-branch multivariate time series anomaly detection method that incorporated contrastive learning. Firstly, it used graph structure learning and feature enhancement to construct relational graphs that captured dynamic correlations among entities. Long short-term memory(LSTM) networks were then employed to extract temporal dependencies and generate temporal encodings. Next, it introduced block reassembly and applied graph convolution operations to extract spatiotemporal relationships across different scales. Finally, the fused relational features underwent joint contrastive training to produce differential representations that effectively distinguished between normal and anomalous patterns. It validated the proposed method through experiments on four public industrial datasets: SWaT, WADI, SWAP, and MSL. The results demonstrate that this method achieves superior F1 scores of 91.63%, 90.60%, 90.06%, and 93.69%, respectively, averaging 1.52 percentage points higher than the MTGFLOW method. The experimental results confirm that this method significantly enhances the extraction of dynamic dependencies and the distinction between normal and anomalous patterns. This validates its effectiveness and advancement in multivariate time series anomaly detection, indicating its broad potential for practical applications.

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.1001-3695.2024.07.0286
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 2
Section: System Development & Application
Pages: 507-513
Serial Number: 1001-3695(2025)02-025-0507-07

Publish History

[2025-02-05] Printed Article

Cite This Article

周丹, 凌捷. 结合对比学习的双分支多维时间序列异常检测方法 [J]. 计算机应用研究, 2025, 42 (2): 507-513. (Zhou Dan, Ling Jie. Integrating contrastive learning dual-branch multivariate time series anomaly detection method [J]. Application Research of Computers, 2025, 42 (2): 507-513. )

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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