Time-series data exists in every corner of real-world systems and services, ranging from satellites in the sky to wearable devices on human bodies. Learning representations by extracting and inferring valuable information from these time series is crucial for understanding the complex dynamics of particular phenomena and enabling informed decisions. With the learned representations, we can perform numerous downstream analyses more effectively. Among several approaches, deep learning has demonstrated remarkable performance in extracting hidden patterns and features from time-series data without manual feature engineering. This survey first presents a novel taxonomy based on three fundamental elements in designing state-of-the-art universal representation learning methods for time series. According to the proposed taxonomy, we comprehensively review existing studies and discuss their intuitions and insights into how these methods enhance the quality of learned representations. Finally, as a guideline for future studies, we summarize commonly used experimental setups and datasets and discuss several promising research directions. An up-to-date corresponding resource is available at https://github.com/itouchz/awesome-deep-time-series-representations.

翻译：时间序列数据存在于现实世界系统与服务的每个角落，从天空中的卫星到人体上的可穿戴设备。通过从这些时间序列中提取和推断有价值信息来学习表示，对于理解特定现象的复杂动态并支持明智决策至关重要。借助学习到的表示，我们能够更有效地执行众多下游分析。在多种方法中，深度学习无需手动特征工程，即可从时间序列数据中提取隐藏模式和特征，展现出卓越性能。本综述首先提出一种新颖的分类体系，其基于设计最先进的通用时间序列表示学习方法的三个基本要素。根据该分类体系，我们全面回顾现有研究，并讨论这些方法如何提升学习表示质量的直觉与洞见。最后，作为未来研究的指南，我们总结了常用的实验设置和数据集，并探讨了几个有前景的研究方向。相应的最新资源可访问 https://github.com/itouchz/awesome-deep-time-series-representations。