PARSI: Parallel Temporal Modeling with Selective Interaction for Irregular Multivariate Time Series Forecasting

Irregular multivariate time series (IMTS) arise when multiple variables are measured at different times and with varying gaps between observations, as commonly occurs in healthcare and finance. These irregularities make it difficult to model how individual variables evolve and how they relate to one another, giving rise to two key challenges: (1) preserving useful short- and long-range temporal patterns that a single temporal resolution or progressive aggregation may obscure, and (2) capturing changing cross-variable dependencies without computing full attention for every variable representation. To address these challenges, we propose PARSI, a forecasting framework that combines parallel temporal modeling with selective cross-variable interaction. First, PARSI encodes different lengths of each variable’s history in parallel and adaptively combines the resulting representations, preserving complementary temporal information. Second, it estimates whether cross-variable dependencies are concentrated on particular variables or distributed broadly, and uses this estimate to select representations for full cross-variable attention. The remaining representations receive a shared global summary, reducing computation while retaining access to information from other variables. Extensive experiments on real-world IMTS forecasting datasets demonstrate that PARSI achieves up to 10.1% relative improvement in forecasting accuracy over representative state-of-the-art baselines while reducing inference time by 25.0%.