NEIN-TSF: Not Every Irregularity Is Noise in Time-Series Forecasting

Modern time-series forecasting models capture increasingly fine-grained historical dynamics, yet not every observed pattern should be extrapolated. Irregular changes may be transient disturbances or genuine regime shifts, while seemingly stable patterns may lose predictive relevance beyond the input window. We propose NEIN-TSF, a unified framework that regulates historical dynamics through forecasting consequence, historical support, and correction utility. Forecast-Damage Ranking Pretraining (FDRP) learns relative teacher-derived forecast damage from matched local counterfactuals, encouraging the backbone to capture predictive consequence rather than input-space deviation. Supported Predictive Evidence (SPE) combines historical retrieval, periodicity, local trends, and persistence to produce bounded, support-conditioned residual corrections. Horizon-Adaptive Evidence Utility (HAEU) calibrates correction strength across prediction positions, while Chronological Forward Utility Selection (CFUS) selects model states on chronologically later validation data. Experiments on fifteen real-world benchmarks across multiple horizons show consistent MSE and MAE improvements over strong recent baselines. Mechanism analyses further show stronger alignment between FDRP-predicted and teacher-derived damage than raw input deviation, positive support-benefit associations, and horizon- and time-dependent correction utility.