Regression Models Meet Foundation Models: A Hybrid-AI Approach to Practical Electricity Price Forecasting
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910044097871872 |
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| author | Qiu, Yunzhong Li, Binzhu Wei, Hao Weng, Shenglin Wang, Chen Pei, Zhongyi Long, Mingsheng Wang, Jianmin |
| author_facet | Qiu, Yunzhong Li, Binzhu Wei, Hao Weng, Shenglin Wang, Chen Pei, Zhongyi Long, Mingsheng Wang, Jianmin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_06726 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Regression Models Meet Foundation Models: A Hybrid-AI Approach to Practical Electricity Price Forecasting Qiu, Yunzhong Li, Binzhu Wei, Hao Weng, Shenglin Wang, Chen Pei, Zhongyi Long, Mingsheng Wang, Jianmin Machine Learning Artificial Intelligence 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. |
| title | Regression Models Meet Foundation Models: A Hybrid-AI Approach to Practical Electricity Price Forecasting |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2603.06726 |