StarTS: State-Aware Dynamic Sample Reweighting for Time Series Forecasting

In time series forecasting, a sample’s training priority, reflected in its loss weight, can change as the model learns. Existing sample-aware methods typically use a single signal, such as noise, uncertainty, or current loss, without jointly considering current fitting state and fitting history. We introduce StarTS, a model-agnostic framework for state-aware dynamic sample reweighting in time series forecasting. Fitting Range Evaluation (FRE) measures difficulty relative to recent losses and penalizes samples beyond a loss-relative fitting boundary, while Mastery Memory (MM) tracks historical evidence of easy fitting. Together, they emphasize samples within the current fitting range with limited mastery evidence and downweight unusually difficult or consistently well-fitted samples. Across eight datasets and nine forecasting backbones, StarTS improves average MSE on every dataset, with average reductions of 0.6%–6.4% across backbones. Across four datasets and three distinct forecasting backbones, StarTS also achieves lower average MSE than two sample-aware training baselines. Code is available at https://anonymous.4open.science/r/StarTs-0E38/.