  Machine learning traditionally assumes that the training and testing data are
distributed independently and identically. However, in many real-world
settings, the data distribution can shift over time, leading to poor
generalization of trained models in future time periods. This paper presents a
novel prompting-based approach to temporal domain generalization that is
parameter-efficient, time-efficient, and does not require access to future data
during training. Our method adapts a trained model to temporal drift by
learning global prompts, domain-specific prompts, and drift-aware prompts that
capture underlying temporal dynamics. Experiments on classification,
regression, and time series forecasting tasks demonstrate the generality of the
proposed approach. The code repository will be publicly shared.
