SEQNet: Support-Aware Irregular Forecasting with Event Memory and Query Routing

Irregular observations are common in healthcare, activity monitoring, and climate analysis. Forecasting from such data requires compressing histories with varying lengths and irregular time intervals into fixed-length representations. However, although historical compression can capture overall temporal trends, it may overlook a small number of locally predictive observations, such as abnormal values and abrupt changes, making these critical local signals difficult to fully exploit. Moreover, when the target variable has insufficient historical observations and information from other variables is needed, different forecasting targets may require different historical evidence and information sources, making fixed information-selection strategies difficult to adapt to changing observation conditions.We propose SEQNet, a forecasting model that preserves both global historical trends and prediction-relevant local evidence. Specifically, SEQNet summarizes historical observations to learn variable-level temporal patterns while constructing a candidate set of local events and dynamically retrieving observations that are relevant to the current forecasting query. When the target variable is insufficiently observed, SEQNet further adapts to its current observation support by assigning different weights to the target variable's own history, information from other variables, and general patterns learned from data.Experiments on PhysioNet, Activity, USHCN, and MIMIC-III show that SEQNet achieves the lowest MSE among the compared methods on all four datasets. On PhysioNet, SEQNet reduces MSE by 24.49% relative to Hi-Patch, the strongest baseline.