Benchmarking Pre-Trained Time Series Models for Electricity Price Forecasting

• URL: http://arxiv.org/abs/2506.08113v2
• Date: Wed, 20 Aug 2025 07:59:08 GMT
• Title: Benchmarking Pre-Trained Time Series Models for Electricity Price Forecasting
• Authors: Timothée Hornek Amir Sartipi, Igor Tchappi, Gilbert Fridgen,
• Abstract summary: We benchmark several state-of-the-art pretrained models against established statistical and machine learning (ML) methods for electricity price forecasting.<n>Using 2024 day-ahead auction (DAA) electricity prices from Germany, France, the Netherlands, Austria, and Belgium, we generate daily forecasts with a one-day horizon.<n>Chronos-Bolt and Time-MoE emerge as the strongest among the TSFMs, performing on par with traditional models.
• Score: 1.1557852082644071
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate electricity price forecasting (EPF) is crucial for effective decision-making in power trading on the spot market. While recent advances in generative artificial intelligence (GenAI) and pre-trained large language models (LLMs) have inspired the development of numerous time series foundation models (TSFMs) for time series forecasting, their effectiveness in EPF remains uncertain. To address this gap, we benchmark several state-of-the-art pretrained models--Chronos-Bolt, Chronos-T5, TimesFM, Moirai, Time-MoE, and TimeGPT--against established statistical and machine learning (ML) methods for EPF. Using 2024 day-ahead auction (DAA) electricity prices from Germany, France, the Netherlands, Austria, and Belgium, we generate daily forecasts with a one-day horizon. Chronos-Bolt and Time-MoE emerge as the strongest among the TSFMs, performing on par with traditional models. However, the biseasonal MSTL model, which captures daily and weekly seasonality, stands out for its consistent performance across countries and evaluation metrics, with no TSFM statistically outperforming it.

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