Long-Horizon Prediction from Multi-Modal and Irregular Behavior Data

In this paper, we study long-horizon prediction from years of multimodal behavioral data and propose an adaptive multi-scale learning framework for effective cross-modal fusion under irregular temporal observations. Discriminative features are first extracted from modality-specific encoders and fused through scale-wise cross attention. To exploit the complementary information across modalities, we introduce a distillation mechanism based on unimodal teachers that learns shared representations through cross-modal contrastive alignment while retaining modality-specific information through correlation minimization. An error-aware adaptive scale router identifies the best-suited temporal resolution for final prediction. In the internal evaluation of an insurance company, with real data collected from $300,000$ users for critical-illness claim prediction, our method outperforms existing multimodal time-series methods. To validate its generality, we also conduct evaluation in the multi-model temporal prediction from two financial benchmarks and achieve state-of-the-art performance.