Contrastive Shapelet Learning for Unsupervised Multivariate Time Series Representation Learning

by   Zhiyu Liang, et al.

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


MHCCL: Masked Hierarchical Cluster-wise Contrastive Learning for Multivariate Time Series

Learning semantic-rich representations from raw unlabeled time series da...

DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection

Time series anomaly detection is critical for a wide range of applicatio...

Learning Robust and Consistent Time Series Representations: A Dilated Inception-Based Approach

Representation learning for time series has been an important research a...

TimeAutoML: Autonomous Representation Learning for Multivariate Irregularly Sampled Time Series

Multivariate time series (MTS) data are becoming increasingly ubiquitous...

TSGBench: Time Series Generation Benchmark

Synthetic Time Series Generation (TSG) is crucial in a range of applicat...

DBT-DMAE: An Effective Multivariate Time Series Pre-Train Model under Missing Data

Multivariate time series(MTS) is a universal data type related to many p...

Learning Timestamp-Level Representations for Time Series with Hierarchical Contrastive Loss

This paper presents TS2Vec, a universal framework for learning timestamp...

Please sign up or login with your details

Forgot password? Click here to reset