Mamba-ADCL：Time series anomaly detection method based on contrastive learning and Mamba

doi:10.11772/j.issn.1001-9081.2025080958

Journal of Computer Applications

Received:2025-08-21 Revised:2025-09-04 Online:2025-10-21 Published:2025-10-21

基于对比学习与Mamba的时间序列异常检测方法Mamba-ADCL

赵志宏,刘栩硕

石家庄铁道大学

通讯作者: 赵志宏
基金资助:
石家庄市市级科技计划资助;国家自然科学基金

Abstract

Key words: Keywords: Mamba, contrastive learning, time series anomaly detection, self-supervised learning, frequency domain

摘要： 针对现有时间序列异常检测方法普遍存在的对标注数据依赖强、复杂场景下建模能力不足等问题，本文提出了一种基于对比学习与Mamba的时间序列异常检测方法Mamba-ADCL。该方法设计了双分支Mamba结构，利用共享权重结构和对比学习策略，有效扩大正常与异常样本间的特征差异，提升模型鲁棒性与泛化能力。同时，提出了时域与频域协同建模的表征方法，将时间序列转换为频域表示，并与时域数据共同构建双视角对比学习机制，从而增强对异常模式的识别能力。在4个公开数据集MSL、SMAP、SWaT和PSM上的实验结果表明，Mamba-ADCL的平均F1-score与当前主流基线方法AnomalyTrans、TFMAE相比分别提升了1.65和0.97个百分点，验证了该方法在时间序列异常检测中的有效性。

关键词: Mamba, 对比学习, 时间序列异常检测, 自监督学习, 频域

CLC Number:

TP391.4

赵志宏刘栩硕. 基于对比学习与Mamba的时间序列异常检测方法Mamba-ADCL[J]. 《计算机应用》唯一官方网站, DOI: 10.11772/j.issn.1001-9081.2025080958.

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Mamba-ADCL：Time series anomaly detection method based on contrastive learning and Mamba

基于对比学习与Mamba的时间序列异常检测方法Mamba-ADCL

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