MorITS: Morphology-Aware Modeling for Irregular Time Series Forecasting

Irregular multivariate time series (IMTS) are prevalent in healthcare, biomechanics, climate science, and astronomy. Observations occur at irregular intervals within variables and at misaligned timestamps across variables. Existing methods typically treat timestamps and missingness masks only as point-level auxiliary information, without explicitly modeling patch-level observation morphology jointly formed by temporal distributions and missingness patterns. This makes it difficult for local semantic extraction and cross-patch interactions to adapt to different observation structures. To address this issue, we propose **MorITS**, a morphology-aware framework for IMTS forecasting. The Observation Morphology Modeling module uses localized Gaussian basis functions to jointly model all timestamps and missingness masks within each patch, constructing morphology representations independent of observed values. The Morphology-Guided Expert Mixture module adaptively extracts patch semantics through morphology-conditioned expert routing and semantic aggregation. The Semantic–Morphology Patch Interaction module combines semantic similarity with inter-patch morphology relations to guide cross-patch interaction. MorITS achieves state-of-the-art forecasting performance on five real-world datasets.