Crude oil is of strategic importance in the world economy, and any change in its price affects economic stability, energy security, and even financial market performance. The high level of volatility in crude oil prices is influenced by geopolitical, economic, and speculative factors; it assigns both difficulties and necessities to the forecasting process. To address this, various forecasting models have been employed to capture the dynamics of oil price movements. Of these, the techniques of mode decomposition prove good in decomposing the complex price series into components to increase the accuracy of forecasting models, which perform the task of breaking down the price series into distinct components: the long-term trend, seasonal variation, and the stochastic short-term fluctuation. This study systematically evaluates and compares commonly used decomposition methods, highlighting the necessity of applying these techniques to enhance forecasting accuracy given the inherent complexity of crude oil prices. Through empirical tests, this study measures the effectiveness of these techniques, providing insights into their relative performance. The findings indicate that decomposition methods significantly enhance forecast accuracy and can be categorized into three tiers based on performance, offering guidance for selecting the most suitable approach for crude oil price forecasting.

Publication:

Energy EconomicsVolume 150, October 2025, 108853

http://dx.doi.org/10.1016/j.eneco.2025.108853

Author:

Mingchen Li

School of Economics and Management, China University of Mining and Technology, Xuzhou, 221116, China

E-mail addresses: limingchen@amss.ac.cn

Haonan Yao

State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China

E-mail addresses: yaohaonan@amss.ac.cn

Shouyang Wang

State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

Center for Forecasting Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

E-mail addresses: sywang@amss.ac.cn

Yunjie Wei

State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

Center for Forecasting Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

Corresponding author

E-mail addresses: weiyunjie@amss.ac.cn