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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 8
2384-2391

Robust anomaly detection for multivariate time series based on adversarial mutual information

Zhang Benchu
Qiao Yan
Hu Rongyao
Anhui Province Key Laboratory of Industry Safety and Emergency Technology, Hefei University of Technology, Hefei 230601, China
DOI: 10.19734/j.issn.1001-3695.2023.11.0579

Abstract

In recent years, anomaly detection for multivariate time series has gradually highlighted its importance in various fields. However, due to the spatiotemporal correlation of the multivariate time series distribution and the inevitable noise in the multivariate time series collected, there exists a non-negligible deviation between the distribution learned by the model and the real distribution, which will affect the detection performance. To tackle the above problems, this paper proposed a new robust anomaly detection model(RADAM) for multivariate time series. The new model used contrastive learning to maximize the mutual information of global and local information of multivariate time series to learn the spatiotemporal correlation among time series. It also used the adaptive weights and a sample filter module to reduce the interference of abnormal samples on the training process, which enabled the model to have high noise resistance ability during training. Finally, through comparing the new model with the-state-of-the-art anomaly detection models on five real-world multivariate time series datasets, the experimental results show that RADAM performs better than other baseline models, which demonstrate that RADAM can significantly improve the performance of anomaly detection on polluted multivariate time series datasets.

Foundation Support

安徽省自然科学基金资助项目(2008085MF203)
中央高校基本科研业务费专项资金资助项目(PA2024GDSK0095)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.11.0579
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 8
Section: Algorithm Research & Explore
Pages: 2384-2391
Serial Number: 1001-3695(2024)08-019-2384-08

Publish History

[2024-02-05] Accepted Paper
[2024-08-05] Printed Article

Cite This Article

张本初, 乔焰, 胡荣耀. 结合对抗互信息的多变量时间序列抗噪异常检测 [J]. 计算机应用研究, 2024, 41 (8): 2384-2391. (Zhang Benchu, Qiao Yan, Hu Rongyao. Robust anomaly detection for multivariate time series based on adversarial mutual information [J]. Application Research of Computers, 2024, 41 (8): 2384-2391. )

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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