  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.

Machine Learning;Artificial Intelligence;Machine Learning