Hybrid Method for Oil Price Prediction Based on Feature Selection and XGBOOST-LSTM

Shucheng Lin, Yue Wang, Haocheng Wei, Xiaoyi Wang and Zhong Wang ()
Additional contact information
Shucheng Lin: College of Management Science, Chengdu University of Technology, Chengdu 610059, China
Yue Wang: College of Management Science, Chengdu University of Technology, Chengdu 610059, China
Haocheng Wei: College of Management Science, Chengdu University of Technology, Chengdu 610059, China
Xiaoyi Wang: College of Management Science, Chengdu University of Technology, Chengdu 610059, China
Zhong Wang: College of Management Science, Chengdu University of Technology, Chengdu 610059, China

Energies, 2025, vol. 18, issue 9, 1-27

Abstract: The accurate and stable prediction of crude oil prices holds significant value, providing insightful guidance for investors and decision-makers. The intricate interplay of factors influencing oil prices and the pronounced fluctuations present significant obstacles within the realm of oil price forecasting. This study introduces a novel hybrid model framework, distinct from the conventional methods, that integrates influencing factors for oil price prediction. First, using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) extract mode components from crude oil prices. Second, using the Adaptive Copula-based Feature Selection (ACBFS), rooted in Copula theory, facilitates the integration of the influencing factors; ACBFS enhances both accuracy and stability in feature selection, thereby amplifying predictive performance and interpretability. Third, low-frequency modes are predicted through an Attention Mechanism-based Long and Short-Term Memory Neural Network (AM-LSTM), optimized using Bayesian Optimization and Hyperband (BOHB). Conversely, high-frequency modes are forecasted using Extreme Gradient Boosting Models (XGboost). Finally, the error correction mechanism further enhances the predictive accuracy. The experimental results show that the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of the proposed hybrid prediction framework are the lowest compared to the benchmark model, at 0.7333 and 1.1069, respectively, which proves that the designed prediction structure has better efficiency and higher accuracy and stability.

Keywords: oil price prediction; ICEEMDAN; ACBFS; BOHB; LSTM; Xgboost (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/9/2246/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/9/2246/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:9:p:2246-:d:1644703

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().