Rethinking Long-Term Time Series Forecasting as Patch-to-Horizon Contribution Learning

Long-term time series forecasting (LTSF) is commonly formulated as learning global representations from historical observations, where the contributions of individual historical patches to different forecasting horizons remain implicit. This representation-centric paradigm not only limits the ability to exploit horizon-dependent historical evidence but also obscures how past observations influence future predictions. To address this issue, we reformulate long-term forecasting as Patch-to-Horizon Contribution Learning (PHCL), a contribution-oriented formulation that explicitly learns the contribution mapping from historical patches to future horizons. Based on this paradigm, we propose Patch-Horizon Lifting (PHL), a contribution-aware forecasting framework that constructs a multi-resolution historical evidence space and adaptively routes resolution-patch candidates to horizon-specific contribution estimators. Extensive experiments on standard long-term forecasting benchmarks demonstrate consistent improvements over representative forecasting methods, validating the effectiveness of horizon-specific contribution learning.