CCo-Patch: Continuous Correction with Temporal Scaffolding for Patch-based Forecasting

Patch-based Transformer models such as PatchTST have become a dominant paradigm for long-term time series forecasting, but patchification introduces an inter-patch continuity gap: local patterns inside each patch are explicitly encoded, while the transitions between neighboring patches must be inferred implicitly, which can result in incoherent predictions across patch boundaries. We propose CCo-Patch, a continuous correction framework that augments a patch-based backbone with a continuous correction branch (hereafter, the Correction branch). The Correction branch extracts a causal low-frequency scaffold—a reference sequence extracted from the original input—through FFT-based filtering, and fuses it with the main branch via a learnable coefficient, with patch-wise mean-centering and Huber optimization further improving robustness. Across seven long-term forecasting benchmarks and four prediction horizons, CCo-Patch achieves the best MSE in 17 out of 28 settings, competitive with or better than the ICLR 2026 PMDformer on most datasets and horizons, with clear gains on Weather, ECL, and ETTh2, and comparable performance on ETTm2. Continuity-oriented evaluation and a patch-granularity analysis further suggest that CCo-Patch improves cross-patch continuity and that this advantage is robust across patch lengths. Code is available in the supplementary material.