CrossForest: Phase-Specialized Learners for Long-Horizon Time Series Forecasting

Time series forecasting supports applications such as traffic planning, energy management and weather forecasting. Forecast accuracy often declines as the prediction horizon increases. To take advantage of the higher accuracy often seen at shorter prediction lengths while preserving the full forecast horizon, we propose CrossForest, a framework that can be applied to different forecasting backbones. It splits a long forecast into phase-specific tasks aligned to a common time origin. For $N$ phases, it routes each timestamp by its residue modulo $N$ to a phase learner with its own parameters. Each learner predicts only its assigned phase: $H/N$ values spaced $N$ time steps apart. Cross-interleaving assembles these outputs into the complete $H$-step forecast. Adjacent sliding windows share most phase tasks, so the training set keeps one copy of each distinct task and retains complete stride-one coverage without repeated computation. We call the combination of globally aligned phase assignment and duplicate-free task construction the Global-Unique protocol. Our experiments show that WPMixer and PatchTST with CrossForest outperform the baselines on multiple forecasting tasks, while CrossForest improves eight of the nine baseline backbones.