使用 Kolmogorov Arnold Networks 的零样本时间序列预测
Zero Shot Time Series Forecasting Using Kolmogorov Arnold Networks
AI总结：
针对能源价格预测模型难以迁移到新市场的问题，提出以 Kolmogorov Arnold Networks 为核心的双残差 N-BEATS 对抗跨域适应模型，实现零样本日前电价预测并取得优于基线的效果。
摘要：
准确的能源价格预测对日前能源市场参与者至关重要，因为它会显著影响其决策过程。尽管基于机器学习的方法在改进这些预测方面已展现出前景，但它们通常仍局限于训练时所针对的特定市场，从而限制了对新市场或未见过市场的适应能力。本文提出一种跨域适应模型，旨在通过在训练阶段学习不同市场之间的市场不变表示来预测能源价格。我们提出一种以 Kolmogorov Arnold networks 为核心的双残差 N-BEATS 网络，用于时间序列预测。这些网络基于 Kolmogorov-Arnold 表示定理，为逼近多元连续函数提供了一种强大方法。该跨域适应模型通过对抗框架生成。模型的有效性在以零样本方式预测日前电价的任务中得到测试。与基线模型相比，我们提出的框架展现出良好结果。通过利用 Kolmogorov-Arnold networks，该模型有可能增强其捕捉能源价格数据中复杂模式的能力，从而提高在多样化市场条件下的预测准确性。这一加入不仅丰富了模型的表示能力，也有助于形成更稳健、更灵活且可适应各种能源市场的预测工具。
英文摘要：
Accurate energy price forecasting is crucial for participants in day-ahead energy markets, as it significantly influences their decision-making processes. While machine learning-based approaches have shown promise in enhancing these forecasts, they often remain confined to the specific markets on which they are trained, thereby limiting their adaptability to new or unseen markets. In this paper, we introduce a cross-domain adaptation model designed to forecast energy prices by learning market-invariant representations across different markets during the training phase. We propose a doubly residual N-BEATS network with Kolmogorov Arnold networks at its core for time series forecasting. These networks, grounded in the Kolmogorov-Arnold representation theorem, offer a powerful way to approximate multivariate continuous functions. The cross domain adaptation model was generated with an adversarial framework. The model's effectiveness was tested in predicting day-ahead electricity prices in a zero shot fashion. In comparison with baseline models, our proposed framework shows promising results. By leveraging the Kolmogorov-Arnold networks, our model can potentially enhance its ability to capture complex patterns in energy price data, thus improving forecast accuracy across diverse market conditions. This addition not only enriches the model's representational capacity but also contributes to a more robust and flexible forecasting tool adaptable to various energy markets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
