\section{Conclusion}\label{sec:conclusion}
In this paper, we theoretically and empirically studied domain generalization under non-stationary environments. We first established the upper bounds of prediction error on target domains, and then proposed a representation learning-based method that learns adaptive invariant representations across source domains. The resulting models trained with the proposed method can generalize well to unseen target domains. Experiments on both synthetic and real data demonstrate the effectiveness of our proposed method.