A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open Challenges
It addresses the problem of generalization in anomaly detection for researchers and practitioners, but it is incremental as a review paper.
This paper reviews self-supervised learning methods for time series anomaly detection, proposing a taxonomy to categorize recent advances and highlighting open challenges in the field.
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.