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.
@article{arxiv.2310.02473,
title = {Prompting-based Temporal Domain Generalization},
author = {Sepidehsadat Hosseini and Mengyao Zhai and Hossein Hajimirsadegh and Frederick Tung},
journal= {arXiv preprint arXiv:2310.02473},
year = {2024}
}