Risk-Controlled Adaptive Native-Scale Fusion: A Backbone-Agnostic Forecasting Framework

Multi-scale modeling is widely used in time series forecasting to capture complementary temporal structures, from fine-grained variations to slower, long-range dynamics. However, temporal aggregation changes the information available at each scale: coarse views suppress high-frequency variation while losing local detail. Requiring coarse branches to reconstruct the finest-grid future, or allowing coarse predictions to modify fine-scale forecasts without sufficient control, can therefore introduce mismatched supervision and harmful cross-scale corrections. To address these, we propose a backbone-agnostic multi-scale forecasting framework that separates native-scale forecasting from prediction-space fusion. Each branch first predicts the future on its native temporal grid, and cross-scale interaction is deferred until these forecasts are completed. We instantiate the framework with two complementary fusion mechanisms: Safe-linear, which combines deterministic alignment with learned participation gates, and Risk-Controlled Adaptive Native-Scale Fusion (RC-ANSF), which introduces bounded adaptive alignment for greater flexibility. A two-stage training procedure further separates scale-specific forecasting from cross-scale fusion. Across three backbone families, eight datasets, four forecasting horizons, the proposed framework consistently improves aggregate forecasting performance. These results support native-scale prediction followed by controlled prediction-space fusion as a reusable strategy for multi-scale forecasting. Our framework is available at https://anonymous.4open.science/r/RC-ANSF-Time-Seires-Forecasting-Framework.