Research on crude oil price forecasting based on computational intelligence

Ming Li; Ying Li; Ming Li; Ying Li

doi:10.3934/DSFE.2023015

Data Science in Finance and Economics

2023, Volume 3, Issue 3: 251-266. doi: 10.3934/DSFE.2023015

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Research on crude oil price forecasting based on computational intelligence

Ming Li ,
Ying Li ^,

Zhongnan University of Economics and Law, Wuhan, 430073, China

Received: 29 May 2023 Revised: 13 July 2023 Accepted: 28 July 2023 Published: 08 August 2023
JEL Codes: C63, E37

The crude oil market, as a complex evolutionary nonlinear driving system, is by nature a highly noisy, nonlinear and deterministic chaotic series of price series. In this paper, a computational intelligence-based portfolio model is constructed to forecast crude oil prices using weekly price data of West Texas intermediate crude oil (WTI) crude oil futures from 2011 to 2021. First, the WTI crude oil price series are decomposed using the ensemble empirical modal decomposition method (EEMD) and the set of component series is reconstructed using the cluster analysis method. Second, the reconstructed series are modeled and predicted using neural network models such as time-delay neural network (TDNN), extreme learning machine (ELM), multilayer perceptron (MLP) and the GM (1, 1) gray prediction algorithm and the output of the model with the best prediction effect for each component is integrated. Finally, the EGARCH model is used to further optimize the predictive power of the combined model and output the final predicted values. The results show that the combined model based on computational intelligence has higher forecasting accuracy than single models such as GM (1, 1), ARIMA, MLP and the combined EEMD-ELM model for forecasting crude oil futures prices.
- crude oil price,
- neural network model,
- gray forecasting algorithm,
- ensemble empirical modal decomposition
Citation: Ming Li, Ying Li. Research on crude oil price forecasting based on computational intelligence[J]. Data Science in Finance and Economics, 2023, 3(3): 251-266. doi: 10.3934/DSFE.2023015

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Abstract

The crude oil market, as a complex evolutionary nonlinear driving system, is by nature a highly noisy, nonlinear and deterministic chaotic series of price series. In this paper, a computational intelligence-based portfolio model is constructed to forecast crude oil prices using weekly price data of West Texas intermediate crude oil (WTI) crude oil futures from 2011 to 2021. First, the WTI crude oil price series are decomposed using the ensemble empirical modal decomposition method (EEMD) and the set of component series is reconstructed using the cluster analysis method. Second, the reconstructed series are modeled and predicted using neural network models such as time-delay neural network (TDNN), extreme learning machine (ELM), multilayer perceptron (MLP) and the GM (1, 1) gray prediction algorithm and the output of the model with the best prediction effect for each component is integrated. Finally, the EGARCH model is used to further optimize the predictive power of the combined model and output the final predicted values. The results show that the combined model based on computational intelligence has higher forecasting accuracy than single models such as GM (1, 1), ARIMA, MLP and the combined EEMD-ELM model for forecasting crude oil futures prices.

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