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：机器学习已经成为时间序列分析的强有力工具。现有的方法通常针对不同的分析任务进行定制，并在应对部分标记和域偏移等实践问题时面临挑战。为实现通用分析并解决上述问题，我们开发了一种新型框架UniTS，它包括自监督表示学习（或预训练）。UniTS组件的设计采用类似于sklearn的API，以允许灵活扩展。我们演示了用户如何使用用户友好的GUI执行分析任务，并展示了UniTS在五个主流任务和两个实际场景上优于传统的任务特定方法，这些方法没有进行自监督预训练的性能。