  Self-supervised learning (SSL) has recently achieved impressive performance
on various time series tasks. The most prominent advantage of SSL is that it
reduces the dependence on labeled data. Based on the pre-training and
fine-tuning strategy, even a small amount of labeled data can achieve high
performance. Compared with many published self-supervised surveys on computer
vision and natural language processing, a comprehensive survey for time series
SSL is still missing. To fill this gap, we review current state-of-the-art SSL
methods for time series data in this article. To this end, we first
comprehensively review existing surveys related to SSL and time series, and
then provide a new taxonomy of existing time series SSL methods by summarizing
them from three perspectives: generative-based, contrastive-based, and
adversarial-based. These methods are further divided into ten subcategories
with detailed reviews and discussions about their key intuitions, main
frameworks, advantages and disadvantages. To facilitate the experiments and
validation of time series SSL methods, we also summarize datasets commonly used
in time series forecasting, classification, anomaly detection, and clustering
tasks. Finally, we present the future directions of SSL for time series
analysis.
