We extend the \emph{neural basis expansion analysis} (NBEATS) to incorporate exogenous factors. The resulting method, called NBEATSx, improves on a well performing deep learning model, extending its capabilities by including exogenous variables and allowing it to integrate multiple sources of useful information. To showcase the utility of the NBEATSx model, we conduct a comprehensive study of its application to electricity price forecasting (EPF) tasks across a broad range of years and markets. We observe state-of-the-art performance, significantly improving the forecast accuracy by nearly 20\% over the original NBEATS model, and by up to 5\% over other well established statistical and machine learning methods specialized for these tasks. Additionally, the proposed neural network has an interpretable configuration that can structurally decompose time series, visualizing the relative impact of trend and seasonal components and revealing the modeled processes' interactions with exogenous factors.

该论文研究了如何将NBEATS模型扩展到考虑外部因素，提出了NBEATSx模型，并将其应用于电价预测任务中，通过对多个市场多年的综合研究，发现它具有非常好的性能，比原始的NBEATS模型提高了近20％，比其他专门用于这些任务的统计和机器学习方法提高了5％。此外，这种神经网络的可解释性使其可以结构分解时间序列，可视化趋势和季节性成分的相对影响，并揭示模型处理外部因素的互动。

使用外生变量的神经基础扩展分析：利用NBEATSx预测电力价格