UniTS: A Universal Time Series Analysis Framework Powered by Self-Supervised Representation Learning
This work addresses practical problems like partial labeling and domain shift in time series analysis for users needing flexible and high-performance tools.
The authors tackled the challenge of task-specific time series analysis methods by developing UniTS, a universal framework using self-supervised representation learning, which outperformed traditional methods on five mainstream tasks and two practical settings.
Machine learning has emerged as a powerful tool for time series analysis. Existing methods are usually customized for different analysis tasks and face challenges in tackling practical problems such as partial labeling and domain shift. To improve the performance and address the practical problems universally, we develop UniTS, a novel framework that incorporates self-supervised representation learning (or pre-training). The components of UniTS are designed using sklearn-like APIs to allow flexible extensions. We demonstrate how users can easily perform an analysis task using the user-friendly GUIs, and show the superior performance of UniTS over the traditional task-specific methods without self-supervised pre-training on five mainstream tasks and two practical settings.