# Regression Models Meet Foundation Models: A Hybrid-AI Approach to Practical Electricity Price Forecasting Source: [Regression Models Meet Foundation Models: A Hybrid-AI Approach to Practical Electricity Price Forecasting]() Collection index: [llms.txt]() Data updated: 2026-03-11T05:00:04.181Z Authors: Yunzhong Qiu, Binzhu Li, Hao Wei, Shenglin Weng, Chen Wang, Zhongyi Pei, Mingsheng Long, Jianmin Wang Published: 2026-03-06T02:06:46.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Electricity Price Forecasting on Shanxi China 12-month summary Day-ahead](): Electricity Price Forecasting; Shanxi China 12-month summary Day-ahead; MSE: 33978.45. - [Electricity Price Forecasting on Shanxi China 12-month summary Real-time](): Electricity Price Forecasting; Shanxi China 12-month summary Real-time; MSE (Val): 44522.21. - [Electricity Price Forecasting on Real-E FR 24-step (test)](): Electricity Price Forecasting; Real-E FR 24-step (test); MSE: 2.9. - [Electricity Price Forecasting on Real-E 24-step (test)](): Electricity Price Forecasting; Real-E DE 24-step (test); MSE (Val): 1.49. ## Original sources and implementation [Original paper]() [PDF]()