Domain generalization aims to design models that can effectively generalize to unseen target domains by learning from observed source domains. Domain generalization poses a significant challenge for time series data, due to varying data distributions and temporal dependencies. Existing approaches to domain generalization are not designed for time series data, which often results in suboptimal or unstable performance when confronted with diverse temporal patterns and complex data characteristics. We propose a novel approach to tackle the problem of domain generalization in time series forecasting. We focus on a scenario where time series domains share certain common attributes and exhibit no abrupt distribution shifts. Our method revolves around the incorporation of a key regularization term into an existing time series forecasting model: domain discrepancy regularization. In this way, we aim to enforce consistent performance across different domains that exhibit distinct patterns. We calibrate the regularization term by investigating the performance within individual domains and propose the domain discrepancy regularization with domain difficulty awareness. We demonstrate the effectiveness of our method on multiple datasets, including synthetic and real-world time series datasets from diverse domains such as retail, transportation, and finance. Our method is compared against traditional methods, deep learning models, and domain generalization approaches to provide comprehensive insights into its performance. In these experiments, our method showcases superior performance, surpassing both the base model and competing domain generalization models across all datasets. Furthermore, our method is highly general and can be applied to various time series models.

领域泛化旨在设计能够通过从观察到的源领域学习来有效泛化到未见过的目标领域的模型。由于不同的数据分布和时间依赖性，域泛化对时间序列数据提出了重大挑战。现有的领域泛化方法并不是针对时间序列数据而设计的，当面对不同的时间模式和复杂的数据特征时，这通常会导致性能不理想或不稳定。我们提出了一种新方法来解决时间序列预测中的域泛化问题。我们关注时间序列域共享某些共同属性并且不表现出突然的分布变化的场景。我们的方法围绕将关键正则化项纳入现有时间序列预测模型：域差异正则化。通过这种方式，我们的目标是在表现出不同模式的不同领域中强制执行一致的性能。我们通过调查各个域内的性能来校准正则化项，并提出具有域难度意识的域差异正则化。我们在多个数据集上展示了我们的方法的有效性，包括来自零售、运输和金融等不同领域的合成和真实时间序列数据集。我们的方法与传统方法、深度学习模型和领域泛化方法进行比较，以提供对其性能的全面见解。在这些实验中，我们的方法展示了卓越的性能，超越了所有数据集的基本模型和竞争域泛化模型。此外，我们的方法具有很强的通用性，可以应用于各种时间序列模型。