Scale-Aware Foundation Priors for Irregular Multivariate Time-Series

Irregular multivariate time series (IMTS) are widely encountered in sequential modeling, yet irregular sampling makes the effective temporal scale vary locally within a single series. However, most existing IMTS methods focus on representing asynchronous and missing observations, still lack explicit local scale adaptation, thereby overlooking this local scale heterogeneity. Foundation models already encode multi-scale temporal structure, but this prior remains locked to a regular sampling grid. To fill this gap, we propose ScaleIMTS, which adapts foundation priors to IMTS in a scale-aware manner. It first conditions these priors on local sampling scales by routing each temporal segment to scale-specific experts. The resulting representations are then refined by an IMTS-specific asynchronous structure: raw events are kept in time-aligned patches to capture local temporal patterns and time-varying inter-variable dependencies, and these features refine the already scale-adapted priors. The refined representations are fed into task-specific decoders to adapt to different downstream tasks. Experiments on 7 datasets demonstrate that ScaleIMTS outperforms state-of-the-art models in IMTS forecasting and classification tasks. Code is available at: https://anonymous.4open.science/r/scale_imts-62E0.