mlsanityData to 14 Oct 2026

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

arXiv 2501.15196 ↗First posted 25 Jan 2025stat.ML cs.LGcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 50 of its 115 references that are in the map. First posted on arXiv 25 Jan 2025.

Builds on

50 of 115 references.

References · 50

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38 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025NeuCoReClass AD: Redefining Self-Supervised Time Series Anomaly Detectioncited by 1
  2. 2025GDformer: Going Beyond Subsequence Isolation for Multivariate Time Series Anomaly Detectioncited by 0
  3. 2026ProtoX-AD: Self-Explainable Time Series Anomaly Detection and Characterizationcited by 0
  4. 2026Supervision Recovery for Time Series Anomaly Detection via Context-Anchored Pairingcited by 0