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 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%2C 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%2C a comprehensive survey for time series SSL is still missing. To fill this gap%2C we review current state-of-the-art SSL methods for time series data in this article. To this end%2C we first comprehensively review existing surveys related to SSL and time series%2C and then provide a new taxonomy of existing time series SSL methods by summarizing them from three perspectives: generative-based%2C contrastive-based%2C and adversarial-based. These methods are further divided into ten subcategories with detailed reviews and discussions about their key intuitions%2C main frameworks%2C advantages and disadvantages. To facilitate the experiments and validation of time series SSL methods%2C we also summarize datasets commonly used in time series forecasting%2C classification%2C anomaly detection%2C and clustering tasks. Finally%2C we present the future directions of SSL for time series analysis.?quality=80&w=800)
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