SPAQR: Sampling-Aware Patch Allocation and Query-conditioned Retrieval for IMTS Forecasting

Irregular multivariate time series (IMTS) forecasting is challenging because observation intervals are uneven within variables and timestamps are unaligned across variables. Patch-based methods can preserve the original observations and their timestamps, yet they commonly determine partitions without allocating a shared patch budget according to cross-variable sampling heterogeneity. Moreover, multi-scale views are typically constructed before a target variable-time query identifies the historical evidence required for a forecast. To this end, we propose SPAQR, Sampling-aware Patch Allocation and Query-conditioned Retrieval for IMTS Forecasting. SPAQR first performs sampling-aware patch allocation, distributing a fixed per-sample patch budget across variables and feasible temporal splits according to observation density, temporal span, and internal gaps. It then applies multi-scale support encoding to retain coarse, boundary, and transition evidence within the resulting fixed supports. Finally, query-conditioned evidence retrieval uses each target variable and forecast time to read global context and softly weight the supports and their internal states without modifying the partition. Across four public IMTS benchmarks and sixteen representative baselines, SPAQR achieves the lowest mean MSE and MAE on every dataset under a common protocol. Ablations and sensitivity studies further characterize the behavior of its allocation and retrieval designs.