A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open Challenges

2025-01-25

AI Summary

Traditional unsupervised methods for time-series anomaly detection suffer from poor generalizability and struggle to adapt to dynamically evolving normal patterns. Method: This paper systematically surveys recent advances in self-supervised learning (SSL) for time-series anomaly detection, introducing the first comprehensive taxonomy tailored to this domain. It unifies modeling paradigms—including masked reconstruction, contrastive learning, temporal discrimination, and prediction consistency—and task construction strategies, while integrating mainstream architectures such as temporal convolutional networks, Transformers, and graph neural networks. Contribution/Results: We propose a structured knowledge graph and open-source a continuously maintained GitHub repository (Awesome-Self-Supervised-Time-Series-Anomaly-Detection). The work explicitly identifies key open challenges and future research directions, establishing the first authoritative classification framework and benchmark reference for SSL-based time-series anomaly detection.

Technology Category

• Machine Learning: Unsupervised & Self-Supervised Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

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.

Problem

• Anomaly Detection
• Time Series Data
• Adaptive Learning

Innovation

• Self-supervised Learning
• Time Series Anomaly Detection
• Classification Method