TASCT: Temporal–Spectral Adaptation and Shape Calibration for Time Series Forecasting

We introduce Temporal–Spectral Adaptation and Shape Calibration for Time Series Forecasting (TASCT), which combines temporal–spectral input adaptation with level-preserving forecast calibration. Its Temporal–Spectral Adapter (TSA) combines centered local-temporal and frequency-based corrections, preserving the historical mean. Its Level-Preserving Calibrator (LPC) combines centered contextual and consistency corrections, preserving the pre-calibration forecast mean. Across five backbones, seven datasets, and four forecasting horizons, TASCT improves three-seed mean MSE in 130 of 140 settings, with an average relative reduction of 4.17%. Experiments with LIFT and CCM further show that TASCT can provide additional gains on top of existing forecasting plugins. These results demonstrate individual and combined gains from input adaptation and output calibration.