TimeCSL: Unsupervised Contrastive Learning of General Shapelets for
  Explorable Time Series Analysis
Zhiyu Liang Chen Liang Zheng Liang Hongzhi Wang Bo Zheng
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.