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 usually employed 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 few labeled samples. Our evaluation includes adapting state-of-the-art visual domain adaptation methods to time series data in addition to the recent methods specifically developed for time series data. We conduct extensive experiments to evaluate 10 state-of-the-art methods on four representative datasets spanning 20 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}.

我们开发了ADATIME评估套件，通过对10种最新颖的域适应方法在20个跨域场景下的四个代表性数据集的广泛实验来系统地和公平地评估不同的域适应方法在时间序列数据上的成功应用，并发现了可以根据实际模型选择方法选择超参数，并为该领域的未来工作奠定了坚实的基础。

ADATIME：面向时间序列数据领域自适应的基准套件