时间序列的自监督学习：对比式还是生成式？
Self-Supervised Learning for Time Series: Contrastive or Generative?
AI总结：
本文全面比较了时间序列中对比式与生成式自监督学习方法，通过公平实验分析其优劣，为选择合适方法提供实用建议。
摘要：
自监督学习（SSL）近来已成为从大规模未标记数据中学习表示的一种强大方法，在时间序列分析中显示出有前景的结果。自监督表示学习可分为两大主流：对比式和生成式。在本文中，我们将对时间序列中的对比式与生成式方法进行全面的比较研究。我们首先分别介绍对比式和生成式SSL的基本框架，并讨论如何获得指导模型优化的监督信号。然后，我们为每种类型实现经典算法（SimCLR与MAE对比），并在公平设置下进行比较分析。我们的结果深入揭示了每种方法的优势与劣势，并为选择适合的SSL方法提供了实用建议。我们还讨论了我们的发现对更广泛的表示学习领域的影响，并提出了未来的研究方向。所有代码和数据已在\url{https://github.com/DL4mHealth/SSL_Comparison}发布。
英文摘要：
Self-supervised learning (SSL) has recently emerged as a powerful approach to learning representations from large-scale unlabeled data, showing promising results in time series analysis. The self-supervised representation learning can be categorized into two mainstream: contrastive and generative. In this paper, we will present a comprehensive comparative study between contrastive and generative methods in time series. We first introduce the basic frameworks for contrastive and generative SSL, respectively, and discuss how to obtain the supervision signal that guides the model optimization. We then implement classical algorithms (SimCLR vs. MAE) for each type and conduct a comparative analysis in fair settings. Our results provide insights into the strengths and weaknesses of each approach and offer practical recommendations for choosing suitable SSL methods. We also discuss the implications of our findings for the broader field of representation learning and propose future research directions. All the code and data are released at \url{https://github.com/DL4mHealth/SSL_Comparison}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
