计算机科学

计算机科学 ›› 2026, Vol. 53 ›› Issue (2): 161-169.doi: 10.11896/jsjkx.241200106

• 数据库&大数据&数据科学 • 上一篇    下一篇

基于时频域注意力的时间序列异常检测模型

徐敬涛, 杨燕, 江永全   

  1. 西南交通大学计算机与人工智能学院 成都 611756
    可持续城市交通智能化教育部工程研究中心 成都 611756
  • 收稿日期:2024-12-16 修回日期:2025-03-17 发布日期:2026-02-10
  • 通讯作者: 杨燕(yyang@swjtu.edu.cn)
  • 作者简介:(kk_xjt@163.com)
  • 基金资助:
    国家自然科学基金(61976247)

Time-Frequency Attention Based Model for Time Series Anomaly Detection

XU Jingtao, YANG Yan, JIANG Yongquan   

  1. School of Computing and Artificial Intelligence,Southwest Jiaotong University,Chengdu 611756,ChinaEngineering Research Center of Sustainable Urban Intelligent Transportation,Ministry of Education,Chengdu 611756,China
  • Received:2024-12-16 Revised:2025-03-17 Online:2026-02-10
  • About author:XU Jingtao,born in 1999,postgraduate.His main research interest is time series anomaly detection and forecasting.
    YANG Yan,born in 1964,Ph.D,professor,Ph.D supervisor, is a member of CCF(No.06877D).Her main research interests include artificial intelligence,big data analysis and mining.
  • Supported by:
    National Natural Science Foundation of China(61976247).

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摘要： 时序数据中复杂的时间依赖关系和有限的异常标签数据,使得时序异常检测成为一项十分具有挑战性的工作。以往大多数工作的聚焦点局限于数据的时域建模上,而忽略并缺失了对时序数据频域信息的提取与利用,造成了一定程度的性能瓶颈。以此为突破点,提出一种基于时域注意力和频域注意力的异常检测模型——TFA-TSAD。该模型首先创新性地对输入数据进行渐进式分解,进而探索数据在不同模式下的异常情况;然后利用精心设计的时频域建模模块,以注意力机制为核心,分别对时序数据的时域信息与频域信息进行高效提取来提高异常检测的性能;此外,在传统误差损失的基础上,采用均方误差加和平均的方式作为损失函数,进一步提高了模型的性能。经过大量的实验验证,该方法在多个数据集上的性能表现卓越,与其他13个基线模型相比,异常检测效果提升显著。

关键词: 时序异常检测, 时域, 频域, 注意力机制, 模式分解

Abstract: Time series anomaly detection is a challenging task due to complex temporal dependencies and limited labeled anomaly data.Previous methods have predominantly focused on modeling in the time domain,overlooking valuable information contained in the frequency domain,resulting in a certain degree of performance bottleneck.Taking this as a breakthrough,this paper proposes a Time-Frequency Attention Based Model for Time Series Anomaly Detection－TFA-TSAD,which firstly innovatively performs progressive decomposition of the input data to explore the anomalies of the data in different modes,and then utilizes the well-designed time-frequency domain modeling module to efficiently extract the time-domain information and frequency-domain information of the origin data respectively,by taking the attention mechanism to improve the performance of anomaly detection.Finally,based on the traditional error loss,a loss function incorporating average MSE is utilized to further improve model performance.Extensive experimental results on multiple datasets demonstrate that the proposed model outperforms 13 other benchmark models with a significant performance.

Key words: Time series anomaly detection, Time domain, Frequency domain, Attention mechanism, Decomposition

中图分类号: 

  • TP391.1
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