Crude Oil Futures Price Forecasting Based on Variational and Empirical Mode Decompositions and Transformer Model

Linya Huang, Xite Yang, Yongzeng Lai, Ankang Zou and Jilin Zhang ()
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Linya Huang: Sichuan Provincial Health Information Center, Chengdu 610000, China
Xite Yang: Business School, Sichuan University, Chengdu 610064, China
Yongzeng Lai: Department of Mathematics, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada
Ankang Zou: Changsha Digital Cloud Chain Technology Co., Ltd., Changsha 410000, China
Jilin Zhang: School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350108, China

Mathematics, 2024, vol. 12, issue 24, 1-16

Abstract: Crude oil is a raw and natural, but nonrenewable, resource. It is one of the world’s most important commodities, and its price can have ripple effects throughout the broader economy. Accurately predicting crude oil prices is vital for investment decisions but it remains challenging. Due to the deficiencies neglecting residual factors when forecasting using conventional combination models, such as the autoregressive moving average and the long short-term memory for prediction, the variational mode decomposition (VMD)-empirical mode decomposition (EMD)-Transformer model is proposed to predict crude oil prices in this study. This model integrates a second decomposition and Transformer model-based machine learning method. More specifically, we employ the VMD technique to decompose the original sequence into variational mode filtering (VMF) and a residual sequence, followed by using EMD to decompose the residual sequence. Ultimately, we apply the Transformer model to predict the decomposed modal components and superimpose the results to produce the final forecasted prices. Further empirical test results demonstrate that the proposed quadratic decomposition composite model can comprehensively identify the characteristics of WTI and Brent crude oil futures daily price series. The test results illustrate that the proposed VMD–EMD–Transformer model outperforms the other three models—long short-term memory (LSTM), Transformer, and VMD–Transformer in forecasting crude oil prices. Details are presented in the empirical study part.

Keywords: crude oil price; variational mode decomposition; empirical mode decomposition; machine learning methods; Transformer model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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