Unsupervised (a.k.a. Self-supervised) representation learning (URL) has emerged as a new paradigm for time series analysis, because it has the ability to learn generalizable time series representation beneficial for many downstream tasks without using labels that are usually difficult to obtain. Considering that existing approaches have limitations in the design of the representation encoder and the learning objective, we have proposed Contrastive Shapelet Learning (CSL), the first URL method that learns the general-purpose shapelet-based representation through unsupervised contrastive learning, and shown its superior performance in several analysis tasks, such as time series classification, clustering, and anomaly detection. In this paper, we develop TimeCSL, an end-to-end system that makes full use of the general and interpretable shapelets learned by CSL to achieve explorable time series analysis in a unified pipeline. We introduce the system components and demonstrate how users interact with TimeCSL to solve different analysis tasks in the unified pipeline, and gain insight into their time series by exploring the learned shapelets and representation.

翻译：无监督（即自监督）表示学习已成为时间序列分析的新范式，因其无需借助通常难以获取的标签即可学习普适性时间序列表示，从而有益于多种下游任务。针对现有方法在表示编码器设计与学习目标构建上的局限性，我们提出了对比形状学习（CSL）——首个通过无监督对比学习通用形状基表示的方法，并在时间序列分类、聚类及异常检测等分析任务中展现出卓越性能。本文开发了TimeCSL这一端到端系统，通过统一流程充分利用CSL学习的通用且可解释的"形状"，实现可探索的时间序列分析。我们介绍了系统组件，并演示用户如何通过统一流程与TimeCSL交互以解决不同分析任务，同时通过探索所学习的形状与表示来洞察时间序列数据。