RestoreTS: Explicit Input Restoration for Robust Time-Series Forecasting

Time series forecasting supports scientific analysis and operational planning across systems that rely on sensors. Yet sensor errors can introduce spikes and sustained level shifts into observed histories, degrading downstream forecasts. Existing approaches reduce corruption effects by changing forecasting architectures or training procedures. However, forecasting robustness alone does not provide an explicitly restored history whose corrections can be inspected. To address this need, we introduce RestoreTS, an input restoration layer that corrects suspicious intervals before an unchanged forecasting backbone. It combines Bayesian online change point detection with temporal return criteria and exposes the affected intervals and applied corrections for inspection. Across eight datasets, five forecasting backbones, four horizons, and five levels of controlled combined corruption, restoration reduces MSE in 39 of the 40 dataset–backbone pairs, while MAE decreases in all 40. Mean reductions across the five backbones range from 6.8% to 19.3% for MSE and from 4.1% to 7.8% for MAE. These results suggest that input restoration can improve corruption robustness across the tested forecasting architectures. Code is available at: https://anonymous.4open.science/r/restorets-review-B9BB/.