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• Time series foundation models (TSFMs) typically underutilize cross-variate correlations and non-periodic patterns that are essential for electricity price forecasting.limitation 85%
• Regression models are limited to future-available inputs, ignoring crucial historical drivers that are unavailable at forecast time.limitation 85%
• FutureBoosting achieves reductions in Mean Absolute Error (MAE) of more than 30% compared to state-of-the-art TSFMs and regression baselines.result 90%
• FutureBoosting enhances regression-based forecasts by integrating forecasted features generated from a frozen TSFM.method 90%
• FutureBoosting leverages the TSFM's ability to model historical patterns and injects these insights as enriched inputs into a downstream regression model.technical 85%
• FutureBoosting establishes a robust, interpretable, and effective solution for practical market participation.market 80%
• The framework is lightweight and plug-and-play for electricity price forecasting.technical 85%
• Ablation studies and explainable AI techniques validate the contribution of forecasted features and elucidate the model's decision-making process.method 85%

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