Benchmarking Pre-Trained Time Series Models for Electricity Price Forecasting

ArXiv ID: 2506.08113 “View on arXiv”

Authors: Timothée Hornek Amir Sartipi, Igor Tchappi, Gilbert Fridgen

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.

Keywords: electricity price forecasting, time series foundation models, generative AI, seasonality, market forecasting

Complexity vs Empirical Score

• Math Complexity: 2.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper is focused on empirical benchmarking of pre-trained models, using real-world data, standard evaluation metrics, and statistical tests like Diebold-Mariano, but the mathematical formulation is minimal, relying more on established libraries and methodologies rather than novel theory.

  flowchart TD
    A["Research Goal: Benchmarking TS Foundation Models<br>vs. Traditional Methods for EPF"] --> B["Data Selection<br>2024 Day-Ahead Auction Prices<br>Germany, France, Netherlands, Austria, Belgium"]
    B --> C["Modeling Process<br>Forecast Daily Prices (1-day Horizon)"]
    C --> D{"Statistical/ML Models<br>e.g., MSTL (Bi-seasonal)"}
    C --> E{"Pre-trained TS Foundation Models<br>Chronos-Bolt, Time-MoE, etc."}
    D --> F["Evaluation & Comparison<br>Performance Metrics across Countries"]
    E --> F
    F --> G["Key Findings & Outcomes"]
    G --> H["TSFMs: Strongest (Chronos-Bolt, Time-MoE)<br>Comparable to Traditional Models"]
    G --> I["Overall Best: MSTL Model<br>Consistent High Performance<br>No TSFM Statistically Superior"]