Forecasting oil commodity spot price in a data-rich environment

Sabri Boubaker, Zhenya Liu () and Yifan Zhang ()
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Sabri Boubaker: Métis Lab
Zhenya Liu: Renmin University of China
Yifan Zhang: Renmin University of China

Annals of Operations Research, 2025, vol. 345, issue 2, No 6, 685-702

Abstract: Abstract Statistical properties that vary with time represent a challenge for time series forecasting. This paper proposes a change point-adaptive-RNN (CP-ADARNN) framework to predict crude oil prices with high-dimensional monthly variables. We first detect the structural breaks in predictors using the change point technique, and subsequently train a prediction model based on ADARNN. Using 310 economic series as exogenous factors from 1993 to 2021 to predict the monthly return on the WTI crude oil real price, CP-ADARNN outperforms competing benchmarks by 12.5% in terms of the root mean square error and achieves a correlation of 0.706 between predicted and actual returns. Furthermore, the superiority of CP-ADARNN is robust for Brent oil price as well as during the COVID-19 pandemic. The findings of this paper provide new insights for investors and researchers in the oil market.

Keywords: Change point detection; Recursive neural network; Oil price prediction; COVID-19 (search for similar items in EconPapers)
JEL-codes: C22 C45 C53 Q43 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10479-022-05004-8

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