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

TL;DR

This work proposes FutureBoosting, a novel paradigm that enhances regression-based forecasts by integrating forecasted features generated from a frozen TSFM and injects these insights as enriched inputs into a downstream regression model.

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.

Figures (10)

• Figure 1: Electricity price characteristics in the Shanxi spot market (15-min). (a) Day-ahead vs real-time forecasts in a representative October 2025 window. (b) Price distribution over the full year 2025, showing heavy tails and extremes.
• Figure 2: Illustration of electricity market trading background. Suppliers and consumers must provide day-ahead plans for day $D+1$ on day $D$. More accurate forecasts from the AI model improve the trading plan's foresight, yielding greater benefits.
• Figure 3: Comparison of forecasting paradigms. Our FutureBoosting paradigm first forecasts future-unavailable exogenous variables using TSFM, then uses these augmented features, along with future-available exogenous variables, for regression-based target forecasting.
• Figure 4: The pipeline of our FutureBoosting framework for electricity price forecasting. TSFM-augmented features, future available exogenous variables, and the grid production plan form an enriched feature set for downstream regression models.
• Figure 5: Adaptation study on Shanxi day-ahead forecasting. We compare zero-shot inference, LoRA fine-tuning, and FutureBoosting regression, with and without future-available exogenous variables.