Recent studies have shown great promise in unsupervised representation learning (URL) for multivariate time series, because URL has the capability in learning generalizable representation for many downstream tasks without using inaccessible labels. However, existing approaches usually adopt the models originally designed for other domains (e.g., computer vision) to encode the time series data and rely on strong assumptions to design learning objectives, which limits their ability to perform well. To deal with these problems, we propose a novel URL framework for multivariate time series by learning time-series-specific shapelet-based representation through a popular contrasting learning paradigm. To the best of our knowledge, this is the first work that explores the shapelet-based embedding in the unsupervised general-purpose representation learning. A unified shapelet-based encoder and a novel learning objective with multi-grained contrasting and multi-scale alignment are particularly designed to achieve our goal, and a data augmentation library is employed to improve the generalization. We conduct extensive experiments using tens of real-world datasets to assess the representation quality on many downstream tasks, including classification, clustering, and anomaly detection. The results demonstrate the superiority of our method against not only URL competitors, but also techniques specially designed for downstream tasks. Our code has been made publicly available at https://github.com/real2fish/CSL.

翻译：近期研究表明，无监督表示学习在多元时间序列领域具有巨大潜力，因其无需使用难以获取的标签即可学习可推广的表示，适用于多种下游任务。然而，现有方法通常采用为其他领域（如计算机视觉）设计的模型对时间序列数据进行编码，并依赖强假设设计学习目标，这限制了其性能表现。针对这些问题，我们提出一种新型无监督表示学习框架，通过流行的对比学习范式学习时间序列特有的基于形状的表示。据我们所知，这是首个探索基于形状的嵌入用于无监督通用表示学习的工作。为实现目标，我们特别设计了统一的形状编码器、具有多粒度对比与多尺度对齐的新颖学习目标，并采用数据增强库提升泛化能力。我们在数十个真实数据集上开展广泛实验，评估表示质量对分类、聚类和异常检测等下游任务的影响。结果表明，我们的方法不仅优于无监督表示学习竞品，甚至超越专门为下游任务设计的技术。我们的代码已在 https://github.com/real2fish/CSL 公开。