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

arXiv:2603.06726v1 Announce Type: new 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

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Yunzhong Qiu, Binzhu Li, Hao Wei, Shenglin Weng, Chen Wang, Zhongyi Pei, Mingsheng Long, Jianmin Wang · · 1 min read · 9 views
#cs.LG #cs.AI

arXiv:2603.06726v1 Announce Type: new 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.

Executive Summary

This article proposes FutureBoosting, a hybrid-AI approach that leverages the strengths of both time series foundation models (TSFMs) and regression models to improve electricity price forecasting. By integrating forecasted features generated from a frozen TSFM into a downstream regression model, FutureBoosting enhances the accuracy of regression-based forecasts while leveraging the historical patterns captured by the TSFM. The authors evaluate their framework on real-world electricity market data, demonstrating significant reductions in Mean Absolute Error (MAE) compared to state-of-the-art TSFMs and regression baselines. The article provides a robust and interpretable solution for practical market participation, establishing a general framework for enhancing regression models with temporal context.

Key Points

  • ▸ FutureBoosting integrates forecasted features from a frozen TSFM into a downstream regression model for improved electricity price forecasting.
  • ▸ The approach leverages the strengths of both TSFMs and regression models to capture historical patterns and feature interactions.
  • ▸ Extensive evaluations demonstrate significant reductions in MAE compared to state-of-the-art TSFMs and regression baselines.

Merits

Strength

The proposed hybrid-AI approach effectively combines the strengths of TSFMs and regression models, leading to improved forecasting accuracy.

Interpretability

The authors provide an interpretable solution by injecting forecasted features from the TSFM into the regression model, enhancing model transparency.

Robustness

FutureBoosting establishes a general framework for enhancing regression models with temporal context, offering a robust solution for practical market participation.

Demerits

Limitation

The proposed approach relies on the availability of historical data and the accuracy of the TSFM, which may not always be feasible or reliable.

Computational Complexity

The integration of the TSFM and regression model may increase computational complexity, potentially limiting the approach's scalability.

Expert Commentary

The article presents a compelling hybrid-AI approach to electricity price forecasting, effectively leveraging the strengths of both TSFMs and regression models. The authors' use of XAI techniques to elucidate the model's decision-making process is particularly noteworthy, highlighting the importance of interpretability in AI-driven forecasting. While the approach has limitations, such as reliance on historical data and potential computational complexity, it demonstrates significant promise for improving forecasting accuracy in practical market participation.

Recommendations

  • ✓ Future researchers should explore the application of FutureBoosting in other domains, such as finance or healthcare, where accurate forecasting is critical.
  • ✓ The authors' use of XAI techniques should be further developed and extended to other AI-driven forecasting approaches, enhancing the interpretability and transparency of these models.

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