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

• URL: http://arxiv.org/abs/2104.05522v2
• Date: Tue, 13 Apr 2021 14:36:36 GMT
• Title: Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx
• Authors: Kin G. Olivares and Cristian Challu and Grzegorz Marcjasz and Rafa{\l} Weron and Artur Dubrawski
• Abstract summary: We study the utility of the NBEATSx model in 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. The proposed neural network has an interpretable configuration that can structurally decompose time series.
• Score: 12.31979377566269
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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