Prompting-based Temporal Domain Generalization
This addresses temporal domain generalization for machine learning applications in dynamic real-world settings, representing a novel method for a known bottleneck.
The paper tackles the problem of poor model generalization due to temporal data distribution shifts by proposing a prompting-based approach that learns global, domain-specific, and drift-aware prompts, achieving parameter-efficient and time-efficient adaptation without needing future data during training.
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.