The scarcity of labeled data is one of the main challenges of applying deep learning models on time series data in the real world. Therefore, several approaches, e.g., transfer learning, self-supervised learning, and semi-supervised learning, have been recently developed to promote the learning capability of deep learning models from the limited time series labels. In this survey, for the first time, we provide a novel taxonomy to categorize existing approaches that address the scarcity of labeled data problem in time series data based on their reliance on external data sources. Moreover, we present a review of the recent advances in each approach and conclude the limitations of the current works and provide future directions that could yield better progress in the field.

翻译：标签数据稀缺是深度学习模型在真实世界时间序列数据上应用时面临的主要挑战之一。因此，近年来涌现了多种方法，例如迁移学习、自监督学习和半监督学习，以提升深度学习模型从有限时间序列标签中学习的能力。在本综述中，我们首次提出一种新颖的分类体系，根据现有方法对外部数据源的依赖程度，对解决时间序列数据中标签稀缺问题的方法进行归类。此外，我们回顾了每种方法的最新进展，总结了当前工作的局限性，并指出了未来可能推动该领域取得更好进步的研究方向。