ADDP: Coordinated Historical Pruning via a Shared Continuous Routing Signal for Irregular Multivariate Time Series Forecasting

Effectively utilizing historical observations is fundamental to irregular multivariate time series forecasting. However, real-world time series often contain redundant segments that distract model attention and degrade forecasting performance. Existing pruning methods for low-information data typically rely on independently designed decision rules for different components, making it difficult for different pruning strategies to coordinate or conflict on the same sample. We propose Adaptive Data-Dependent Pruning (ADDP), a framework that uses a shared signal to guide multiple pruning decisions during training. Specifically, ADDP summarizes each sample's historical variation in a shared signal that guides subsequent pruning decisions. Lightweight learnable calibrators then use this signal to determine the redundancy of historical segments, the amount of information shared between aggressive and conservative pruning views, and the weights assigned to these views. Based on these decisions, ADDP constructs two complementary views that retain different amounts of historical information and jointly trains a shared forecasting model. Experiments on PhysioNet, Activity, USHCN, and MIMIC-III show that ADDP consistently outperforms competing methods, achieving the lowest mean MSE on each of the four datasets. On MIMIC-III, ADDP reduces MSE by 5.05% relative to Hi-Patch, the strongest evaluated baseline.