Perceive, Route, Modulate: Dynamic Pattern Recalibration for Time Series Forecasting

Local temporal patterns in real-world time series continuously shift across regimes. While existing forecasting backbones express input-dependent responses through shared transformations, they lack an explicit mechanism to separate shared representation learning from local feature-gain adaptation. We introduce Dynamic Pattern Recalibration (DPR), a backbone-agnostic mechanism that realizes this separation via token-level modulation. Through a lightweight “Perceive-Route-Modulate” pipeline, DPR computes a soft-routing distribution over a compact learned response basis, generating a context-dependent modulation vector that recalibrates hidden states via a residual Hadamard product. Across ten forecasting backbones, DPR matches or improves the raw backbone’s MSE in 87 of 95 backbone-dataset pairs (91.6%), with larger gains associated with greater local volatility variation. Its minimalist standalone instantiation, DPRNet, achieves strong forecasting performance across 11 benchmarks with low measured computational cost. Our code is available at https://anonymous.4open.science/r/DPRNet.