Engineering

Aerospace engineering

Materials science

Time series

Basis (linear algebra)

Operating system

Range (aeronautics)

Economics

Set (abstract data type)

Computer science

Code (set theory)

Source code

Mathematics

Econometrics

Artificial intelligence

Geometry

Composite material

Programming language

Artificial neural network

Electricity

Machine learning

Electrical engineering

International Journal of Forecasting

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.

Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx