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.

翻译：机器学习已成为时间序列分析的强大工具。现有方法通常针对不同分析任务定制化设计，并在处理部分标签标注、领域偏移等实际问题时面临挑战。为实现通用分析并解决上述问题，我们开发了UniTS——一种融合自监督表示学习（或预训练）的新型框架。UniTS的组件采用类似sklearn的API设计，支持灵活扩展。我们展示了用户如何通过用户友好的图形界面轻松执行分析任务，并证明在五种主流任务和两种实际场景中，UniTS相较于未使用自监督预训练的传统任务特定方法具有更优越的性能。