Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (6A): 250600105-10.doi: 10.11896/jsjkx.250600105

• Information Security • Previous Articles     Next Articles

Robust Time Series Anomaly Detection Model Based on Multi-view Cross Filtering

ZHANG Juling1,3, ZHAO Yibing2, WANG Sheng1,3, XI Ning4, SHE Wenkui5   

  1. 1 State Grid Sichuan Electric Power Research Institute, Chengdu 610043,China
    2 State Grid Sichuan Electric Power Company,Chengdu 610043,China
    3 Power System Security and Operation Key Laboratory of Sichuan Province,Chengdu 610043,China
    4 State Grid Tianfu New Area Power Supply Company,Chengdu 610200,China
    5 Aostar Information Technology Co.,Ltd.,Chengdu 610200,China
  • Online:2026-06-16 Published:2026-06-12
  • About author:ZHANG Juling,born in 1990,senior engineer.Her main research interests include grid network security attack and defense,data security,Internet of Things security and industrial control security.
    SHE Wenkui,born in 1982,master,se-nior engineer.His main research in-terests include cloud computing,power Internet of Things.
  • Supported by:
    Scientific Research Foundation of State Grid Sichuan Electric Power Company(52199723002P).

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Abstract: Amid the rapid advancement of digital transformation and intelligent technologies,fields such as industrial manufactu-ring,financial transactions,and energy management increasingly rely on vast amounts of time series data to support critical decision-making.The sudden occurrence of anomalous events not only poses a threat to system performance but also severely affects overall security.Thus,efficiently identifying anomalies from large-scale,structurally complex data has become a pressing challenge.This paper focuses on the issue of time series anomaly detection by investigating the interference caused by noise in industrial datasets during model training and proposing an improved strategy.Data collected in industrial environments often exhibit characteristics such as high dimensionality and the presence of multiple noises.When noise is incorporated into the training samples,the learning process of the model is easily disrupted,leading to reduced robustness.Previous studies mainly adopted a single indicator to identify and filter noisy samples,a method that may introduce cumulative errors during training and consequently affect the accuracy of anomaly detection.To address the aforementioned issues,this paper proposes a robust time series anomaly detection model based on multi-view cross filtering(MVCF-AD).The model first introduces the concept of non-neighbor attention and combines it with reconstruction error to construct a dual-indicator system for noise discrimination.Subsequently,a multi-view cross filtering strategy is built,and a dual-network parallel training approach is employed.By utilizing loss ranking,the model dynamically identifies and filters noisy samples.Experimental results demonstrate that MVCF-AD exhibits excellent detection performance and robustness under various noise levels,thereby proving its effectiveness in addressing the noise issue in datasets.

Key words: Time series, Anomaly detection, Data noise, Robust learning

CLC Number: 

  • TP183
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