标题： PriceFM：概率电力价格预测的基础模型 显示英文标题

标题： PriceFM: Foundation Model for Probabilistic Electricity Price Forecasting

摘要： 欧洲的电价预测面临独特的挑战，这是由于该大陆日益整合和物理互联的电力市场。 尽管深度学习和基础模型的最新进展在一般时间序列预测方面取得了显著改进，但大多数现有方法未能捕捉到电力市场中固有的复杂空间相互依赖性和不确定性。 在本文中，我们通过引入一个覆盖24个欧洲国家（38个地区）的全面且最新的数据集来解决这些限制，数据时间跨度从2022-01-01到2025-01-01。 在此基础上，我们提出了PriceFM，这是一种时空基础模型，结合了基于图的归纳偏差，以捕捉互联电力市场之间的空间相互依赖性。 该模型旨在进行多区域、多时间步和多分位数的概率电价预测。 广泛的实验和消融研究证实了该模型的有效性，持续优于竞争基线，并突显了空间上下文在电力市场中的重要性。 数据集和代码可在https://github.com/runyao-yu/PriceFM找到。 显示英文摘要

摘要： Electricity price forecasting in Europe presents unique challenges due to the continent's increasingly integrated and physically interconnected power market. While recent advances in deep learning and foundation models have led to substantial improvements in general time series forecasting, most existing approaches fail to capture the complex spatial interdependencies and uncertainty inherent in electricity markets. In this paper, we address these limitations by introducing a comprehensive and up-to-date dataset across 24 European countries (38 regions), spanning from 2022-01-01 to 2025-01-01. Building on this groundwork, we propose PriceFM, a spatiotemporal foundation model that integrates graph-based inductive biases to capture spatial interdependencies across interconnected electricity markets. The model is designed for multi-region, multi-timestep, and multi-quantile probabilistic electricity price forecasting. Extensive experiments and ablation studies confirm the model's effectiveness, consistently outperforming competitive baselines and highlighting the importance of spatial context in electricity markets. The dataset and code can be found at https://github.com/runyao-yu/PriceFM.