综述自监督学习在时间序列异常检测中的进展与挑战
A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open Challenges
- 按核心特征分类自监督方法，梳理技术脉络
- 指出传统方法泛化差、易过拟合的痛点
- 适合关注时序异常检测研究者参考
时间序列异常检测因数据的时序性和动态性面临诸多挑战。传统无监督方法常因在训练中过度拟合已知正常模式而难以泛化，难以适应未见的正常状态。为应对这一局限，时间序列自监督学习方法受到关注，被视为提升异常检测性能的潜在解决方案。本文系统综述了近期基于自监督学习的时间序列异常检测方法，提出一种分类体系，依据方法的主要特征进行归类，有助于清晰理解该领域的多样性。本综述包含的详细信息及后续更新内容，可访问以下 GitHub 仓库获取：https://github.com/Aitorzan3/Awesome-Self-Supervised-Time-Series-Anomaly-Detection。
原文摘要 · Abstract (English)
Time series anomaly detection presents various challenges due to the sequential and dynamic nature of time-dependent data. Traditional unsupervised methods frequently encounter difficulties in generalization, often overfitting to known normal patterns observed during training and struggling to adapt to unseen normality. In response to this limitation, self-supervised techniques for time series have garnered attention as a potential solution to undertake this obstacle and enhance the performance of anomaly detectors. This paper presents a comprehensive review of the recent methods that make use of self-supervised learning for time series anomaly detection. A taxonomy is proposed to categorize these methods based on their primary characteristics, facilitating a clear understanding of their diversity within this field. The information contained in this survey, along with additional details that will be periodically updated, is available on the following GitHub repository: https://github.com/Aitorzan3/Awesome-Self-Supervised-Time-Series-Anomaly-Detection.
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