Unsupervised domain adaptation methods aim to generalize well on unlabeled test data that may have a different (shifted) distribution from the training data. Such methods are typically developed on image data, and their application to time series data is less explored. Existing works on time series domain adaptation suffer from inconsistencies in evaluation schemes, datasets, and backbone neural network architectures. Moreover, labeled target data are often used for model selection, which violates the fundamental assumption of unsupervised domain adaptation. To address these issues, we develop a benchmarking evaluation suite (AdaTime) to systematically and fairly evaluate different domain adaptation methods on time series data. Specifically, we standardize the backbone neural network architectures and benchmarking datasets, while also exploring more realistic model selection approaches that can work with no labeled data or just a few labeled samples. Our evaluation includes adapting state-of-the-art visual domain adaptation methods to time series data as well as the recent methods specifically developed for time series data. We conduct extensive experiments to evaluate 11 state-of-the-art methods on five representative datasets spanning 50 cross-domain scenarios. Our results suggest that with careful selection of hyper-parameters, visual domain adaptation methods are competitive with methods proposed for time series domain adaptation. In addition, we find that hyper-parameters could be selected based on realistic model selection approaches. Our work unveils practical insights for applying domain adaptation methods on time series data and builds a solid foundation for future works in the field. The code is available at \href{https://github.com/emadeldeen24/AdaTime}{github.com/emadeldeen24/AdaTime}.

翻译：无监督域适配方法旨在对与训练数据分布不同（偏移）的无标签测试数据实现良好泛化。此类方法通常基于图像数据开发，其在时间序列数据上的应用尚缺乏深入探索。现有时间序列域适配工作存在评估方案、数据集和骨干神经网络架构不一致的问题。此外，模型选择常依赖有标签目标数据，这违背了无监督域适配的基本假设。为解决上述问题，我们开发了一套基准测试套件（AdaTime），以系统且公平地评估时间序列数据上的不同域适配方法。具体而言，我们标准化了骨干神经网络架构和基准数据集，同时探索了无需标签数据或仅需少量标签样本即可运行的更贴合实际的模型选择方法。评估范畴涵盖将前沿视觉域适配方法迁移至时间序列数据，以及近期专为时间序列数据设计的方法。我们开展了大规模实验，在跨越50个跨域场景的五个代表性数据集上评估了11种前沿方法。结果表明，通过谨慎选择超参数，视觉域适配方法可与针对时间序列域适配提出的方法相竞争。此外，我们发现基于贴合实际的模型选择方法可确定超参数。本研究揭示了在时间序列数据上应用域适配方法的实践洞见，为该领域未来研究奠定了坚实基础。代码开源地址：\href{https://github.com/emadeldeen24/AdaTime}{github.com/emadeldeen24/AdaTime}。