mlsanityData to 7 Oct 2026

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

arXiv 2104.05522 ↗First posted 12 Apr 2021cs.LG cs.AI stat.MLcited by 63

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Abstract (arXiv)

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.

Key facts. Cited by 63 papers in the map (data through 7 Oct 2026). Builds on 22 of its 104 references that are in the map. First posted on arXiv 12 Apr 2021.

Builds on

22 of 104 references.

References · 22

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10 more
  1. 2016Language Modeling with Gated Convolutional Networkscited by 688
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  3. 2017A Multi-Horizon Quantile Recurrent Forecastercited by 211
  4. 2020Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmarkcited by 105
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 11; 2023: 6; 2024: 8; 2025: 21; 2026: 13.

63 papers.

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  5. 2025Aurora: Towards Universal Generative Multimodal Time Series Forecastingcited by 28
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  7. 2022Preformer: Predictive Transformer with Multi-Scale Segment-wise Correlations for Long-Term Time Series Forecastingcited by 23
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  9. 2021Probabilistic Hierarchical Forecasting with Deep Poisson Mixturescited by 16
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  32. 2021DMIDAS: Deep Mixed Data Sampling Regression for Long Multi-Horizon Time Series Forecastingcited by 0
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