Online Multivariate Time Series Anomaly Detection Method Based on Contrastive Learning

被引:0
|
作者
Dong, Xiyao [1 ]
Liu, Hui [1 ]
Du, Junzhao [1 ]
Wang, Zhengkai [1 ]
Wang, Cheng [1 ]
机构
[1] Xidian Univ, Sch Comp Sci & Technol, Xian 710071, Peoples R China
来源
ADVANCED INTELLIGENT COMPUTING TECHNOLOGY AND APPLICATIONS, PT XIII, ICIC 2024 | 2024年 / 14874卷
基金
中国国家自然科学基金;
关键词
Anomaly detection; Autocorrelation; Contrastive learning; Online learning;
D O I
10.1007/978-981-97-5618-6_39
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With the swift progression of industrial automation and Internet of Things technologies, the importance of multivariate time series anomaly detection has markedly increased, serving as a vital tool for identifying abnormal behaviors within complex datasets to prevent potential risks. Traditional anomaly detection methods often struggle to deal with multivariable and unlabeled data environments, especially in the context of real-time dynamic data streams, where traditional models require frequent retraining to adapt to new anomaly patterns. To address this challenge, our work proposes an online anomaly detection model for multivariate time series based on a contrastive learning framework:ODAnomaly, utilizing a dual autocorrelation mechanism to effectively extract features of normal data and distinguish anomalous data. The model features an online learner that uses gradient updates and Pearson correlation coefficients to rapidly adapt to new anomaly patterns, boosting its real-time learning efficiency. A contrastive loss function, informed by homoscedastic uncertainty, aids in anomaly detection through data representation. This approach reduces reliance on extensively labeled data and enhances the model's adaptability and accuracy in real-time data streams. It provides an efficient and cost-effective solution for advancing multivariate time series anomaly detection in both research and practical applications.
引用
收藏
页码:468 / 479
页数:12
相关论文
共 50 条
  • [21] Improving Deep Learning Based Anomaly Detection on Multivariate Time Series Through Separated Anomaly Scoring
    Lundstrom, Adam
    O'Nils, Mattias
    Qureshi, Faisal Z.
    Jantsch, Axel
    IEEE ACCESS, 2022, 10 : 108194 - 108204
  • [22] Time Series Anomaly Detection Using Contrastive Learning based One-Class Classification
    Lee, Yeseul
    Byun, Yunseon
    Baek, Jun-Geol
    2023 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE IN INFORMATION AND COMMUNICATION, ICAIIC, 2023, : 330 - 335
  • [23] Machine Learning-Based Anomaly Detection for Multivariate Time Series With Correlation Dependency
    Chauhan, Shashank
    Lee, Sudong
    IEEE ACCESS, 2022, 10 : 132062 - 132070
  • [24] Machine Learning-Based Anomaly Detection for Multivariate Time Series with Correlation Dependency
    Chauhan, Shashank
    Lee, Sudong
    IEEE Access, 2022, 10 : 132062 - 132070
  • [25] DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection
    Yang, Yiyuan
    Zhang, Chaoli
    Zhou, Tian
    Wen, Qingsong
    Sun, Liang
    PROCEEDINGS OF THE 29TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2023, 2023, : 3033 - 3045
  • [26] TimeAutoAD: Autonomous Anomaly Detection With Self-Supervised Contrastive Loss for Multivariate Time Series
    Jiao, Yang
    Yang, Kai
    Song, Dongjing
    Tao, Dacheng
    IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING, 2022, 9 (03): : 1604 - 1619
  • [27] Contrastive Time Series Anomaly Detection by Temporal Transformations
    Li, Bin
    Mueller, Emmanuel
    2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN, 2023,
  • [28] Generative Anomaly Detection in Multivariate Time Series
    Hoh, M.
    Schöttl, A.
    Schaub, H.
    Leuze, N.
    Automation, Robotics and Communications for Industry 4.0/5.0, 2023, 2023 : 171 - 174
  • [29] Self-attention-based graph transformation learning for anomaly detection in multivariate time series
    Wang, Qiushi
    Zhu, Yueming
    Sun, Zhicheng
    Li, Dong
    Ma, Yunbin
    COMPLEX & INTELLIGENT SYSTEMS, 2025, 11 (05)
  • [30] An anomaly detection model for multivariate time series with anomaly perception
    Wei, Dong
    Sun, Wu
    Zou, Xiaofeng
    Ma, Dan
    Xu, Huarong
    Chen, Panfeng
    Yang, Chaoshu
    Chen, Mei
    Li, Hui
    PEERJ COMPUTER SCIENCE, 2024, 10