OrderFusion+：日内电力市场中概率性买卖价格轨迹预测
OrderFusion+: Probabilistic Buy--Sell Price Trajectory Forecasting in Intraday Electricity Markets
AI总结：
针对日内电力市场，提出OrderFusion+开源深度学习模型，融合目标与相邻交付产品订单，预测概率性买卖价格轨迹，并通过动态掩蔽机制分析市场效率。
AI中文摘要：
日内电力市场使参与者能够在临近交付时调整能源头寸，而价格预测对于支持交易、灵活电力资源的调度以及日益响应式的消费者至关重要。预测模型规范已从使用宏观特征（如可再生能源发电量和负荷）发展到连续订单簿的微观特征。近期先进的深度学习模型OrderFusion显式建模了微观层面的买卖订单簿交互。然而，尽管其预测性能优越，但它一次仅考虑一个交付产品，忽略了相邻产品的信息。此外，在预测流动性强的德国ID3、ID2和ID1产品等聚合价格指数时，它遗漏了价格轨迹中包含的信息。相比之下，预训练的时间序列基础模型在金融、可再生能源和日前电价预测中已显示出成功。然而，它们在日内订单簿数据上的表现仍是一个具有相当实际重要性的开放研究问题。在本文中，我们提出了OrderFusion+，一个开源深度学习模型，它结合了目标交付产品和相邻交付产品的历史订单，以预测概率性的买卖价格轨迹。我们将OrderFusion+与预测基线和预训练基础模型进行基准比较，并通过设计的动态掩蔽机制研究动态市场条件，揭示市场效率的见解。实现和预测结果可在此https URL找到。
英文摘要：
Intraday electricity markets enable participants to adjust energy positions close to delivery, with price forecasts necessary to support trading and the scheduling of flexible electricity resources as well as increasingly responsive consumers. Forecasting model specifications have progressed from using macro-features, such as renewable generation and load, to the micro-features of continuous orderbooks. A recent advanced deep learning model, OrderFusion, explicitly models micro-level buy-sell orderbook interactions. However, despite its superior comparative forecasting performance, it considers only one delivery product at a time, ignoring neighboring-product information. Moreover, when forecasting aggregated price indices such as the liquid German ID3, ID2, and ID1 products, it omits the information contained in price trajectories. In contrast, pretrained time-series foundation models have shown success in financial, renewable-energy, and day-ahead electricity price forecasting. However, their performance on intraday orderbook data remains an open research question of considerable practical importance. In this paper, we propose OrderFusion+, an open-source deep learning model that combines historical orders from the target and neighboring delivery products to forecast probabilistic buy-sell price trajectories. We benchmark OrderFusion+ against forecasting baselines and pretrained foundation models, and investigate dynamic market conditions through the designed dynamic masking mechanism, revealing insights into market efficiency. The implementation and forecasts can be found at: https://runyao-yu.com/OrderFusion/
