We extend the 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. To assist related work we made the code available in https://github.com/cchallu/nbeatsx.

翻译：我们扩展了神经基础扩展分析(NBEATS)以纳入外在因素。由此得出的方法,称为NBEATSx,改善了一个表现良好的深层学习模式,通过包括外源变量和使其能够整合多种有用信息来源,扩大了能力。为了展示NBEATSx模型的效用,我们对其应用于电力价格预测(EPF)任务的情况进行了广泛的年份和市场的全面研究。我们观察了最新业绩,大大提高了预测的准确性,比最初的NBEATS模型提高了近20%,比为这些任务专门制定的其他成熟的统计和机器学习方法提高了多达5%。此外,拟议的神经网络有一个可解释的配置,可以从结构上分解时间序列,直观趋势和季节组成部分的相对影响,并揭示模型过程与外源因素的相互作用。为了协助相关工作,我们在https://github.com/cchallu/nbeatex中提供了代码。