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Forecasting crude oil futures price with energy uncertainty: Evidence from machine learning methods

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  • Xiaolu Wei

Abstract

Energy related uncertainty has significant influence on crude oil market. To explore the influence, this paper investigates the predictive ability of the Energy-Related Uncertainty Index (EUI), over and above standard macroeconomic predictors, in forecasting crude oil prices using an array of machine learning methods. We find that EUI has a significant impact on crude oil prices. Moreover, machine learning methods combined with EUI performed better than the linear regression method due to a lower rate of prediction errors. Among these methods, the Random Forest (RF) model with EUI performs better in the short term, while the Attention-enhanced Long Short-Term Memory (Attention-LSTM) model with EUI has more substantial predictive power in the long term. These empirical results pass a series of robustness tests. Our findings have important implications for both regulators and investors in the crude oil market.

Suggested Citation

  • Xiaolu Wei, 2026. "Forecasting crude oil futures price with energy uncertainty: Evidence from machine learning methods," PLOS ONE, Public Library of Science, vol. 21(2), pages 1-29, February.
    • Handle: RePEc:plo:pone00:0341496
    DOI: 10.1371/journal.pone.0341496
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    References listed on IDEAS

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