GRAFT: Decoupling Dynamic Recovery from Structural Prediction in Time Series Forecasting

Modern long-term forecasters are typically trained with global point-wise objectives, whose error reduction is dominated by high-energy, low-frequency periodic structures. This bias favors structural forecasts, while high-utility sparse local dynamics are systematically under-rewarded and risky to optimize. We therefore propose GRAFT, a general forecasting enhancement paradigm that decouples dynamic recovery from the structure-dominated role of a base forecaster. GRAFT treats dynamic recovery as a distinct forecasting target with its own modeling process, while the existing forecaster retains its full prediction as a structural anchor. Specifically, historical context–response pairs provide evidence for recurrent local dynamics, which GRAFT selectively grafts onto the base forecast under phase-, channel-, and confidence-aware constraints. Complementary objectives explicitly supervise local changes and global spectral structure, guiding dynamic recovery beyond point-wise accuracy alone. Across seven benchmarks, two metrics, four forecasting horizons, and eight representative backbones, GRAFT improves over the corresponding backbone in 85.7% of all comparisons, with average MSE/MAE reductions of 3.88%/3.00%. Dynamic-window analyses further show that the gains arise from recovering local dynamics while preserving the stable backbone. These results validate the effectiveness of decoupled dynamic recovery and demonstrate consistent enhancement. More broadly, they suggest a new principle for high-utility forecasting: predict the backbone, then graft the dynamics. Anonymous code is available at: https://anonymous.4open.science/r/GRAFT-5E8F/.