Regression Models Meet Foundation Models: A Hybrid-AI Approach to Practical Electricity Price Forecasting

• URL: http://arxiv.org/abs/2603.06726v1
• Date: Fri, 06 Mar 2026 02:06:46 GMT
• Title: Regression Models Meet Foundation Models: A Hybrid-AI Approach to Practical Electricity Price Forecasting
• Abstract summary: We propose FutureBoosting, a novel paradigm that enhances regression-based forecasts.<n>We instantiate this paradigm into a lightweight, plug-and-play framework for electricity price forecasting.<n>Our framework consistently outperforms state-of-the-art TSFMs and regression baselines.
• Score: 39.91788164419845
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Electricity market prices exhibit extreme volatility, nonlinearity, and non-stationarity, making accurate forecasting a significant challenge. While cutting-edge time series foundation models (TSFMs) effectively capture temporal dependencies, they typically underutilize cross-variate correlations and non-periodic patterns that are essential for price forecasting. Conversely, regression models excel at capturing feature interactions but are limited to future-available inputs, ignoring crucial historical drivers that are unavailable at forecast time. To bridge this gap, we propose FutureBoosting, a novel paradigm that enhances regression-based forecasts by integrating forecasted features generated from a frozen TSFM. This approach leverages the TSFM's ability to model historical patterns and injects these insights as enriched inputs into a downstream regression model. We instantiate this paradigm into a lightweight, plug-and-play framework for electricity price forecasting. Extensive evaluations on real-world electricity market data demonstrate that our framework consistently outperforms state-of-the-art TSFMs and regression baselines, achieving reductions in Mean Absolute Error (MAE) of more than 30% at most. Through ablation studies and explainable AI (XAI) techniques, we validate the contribution of forecasted features and elucidate the model's decision-making process. FutureBoosting establishes a robust, interpretable, and effective solution for practical market participation, offering a general framework for enhancing regression models with temporal context.

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