  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 achieve universal analysis and address the aforementioned problems,
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.
