Regime-Aware Cross-Market Electricity Price Forecasting with TimesFM and Large Language Models

Electricity price forecasting is challenging because electricity markets are influenced by complex and rapidly changing interactions among demand, renewable generation, weather conditions, transmission constraints, market rules, and operational events. Time-series foundation models such as TimesFM provide a promising backbone for modeling numerical temporal dynamics, but they primarily rely on historical observations and may struggle when market regimes change. Large language models offer complementary capabilities for interpreting unstructured information, including regulatory updates, market announcements, operational reports, and extreme-event descriptions, but they are not designed to directly produce calibrated numerical forecasts. We propose a regime-aware cross-market electricity price forecasting framework that combines TimesFM with large language models. TimesFM models the temporal dynamics of electricity prices and related numerical variables, while a large language model converts time-stamped textual information into structured market-regime representations. These representations are integrated through a lightweight market-adaptation module, allowing a shared forecasting backbone to capture common patterns across markets while adapting to market-specific characteristics. The framework further supports probabilistic forecasting and explicitly models price spikes and uncertainty associated with regime transitions. We design a leakage-controlled evaluation protocol and conduct cross-market transfer experiments to assess the model’s robustness under distribution shifts and limited data availability. The study evaluates forecasting accuracy, probabilistic calibration, extreme-price prediction, transferability, and computational efficiency, providing a systematic investigation of how language-derived market knowledge can enhance time-series foundation models for electricity price forecasting.