Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx
将NBEATS方法扩展为NBEATSx，引入外生变量提升预测精度，在电价预测任务上比原模型准确率提高近20%，且模型可解释，能分解时间序列的趋势和季节成分。
We extend 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 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 a dedicated repository.