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Machine learning has emerged as a powerful tool for time series analysis.Existing methods are usually customized for different analysis tasks and facechallenges in tackling practical problems such as partial labeling and domainshift. To achieve universal analysis and address the aforementioned problems,we develop UniTS, a novel framework that incorporates self-supervisedrepresentation learning (or pre-training). The components of UniTS are designedusing sklearn-like APIs to allow flexible extensions. We demonstrate how userscan easily perform an analysis task using the user-friendly GUIs, and show thesuperior performance of UniTS over the traditional task-specific methodswithout self-supervised pre-training on five mainstream tasks and two practicalsettings.

UniTS: A Universal Time Series Analysis Framework with Self-supervised Representation Learning