Multi-step-ahead crude oil price forecasting based on two-layer decomposition technique and extreme learning machine optimized by the particle swarm optimization algorithm

Tingting Zhang, Zhenpeng Tang, Junchuan Wu, Xiaoxu Du and Kaijie Chen

Energy, 2021, vol. 229, issue C

Abstract: The prediction of crude oil prices has important research significance. The paper contributes to the literature of hybrid models for forecasting crude oil prices. We apply ensemble empirical mode decomposition (EEMD) to decompose the residual term (RES), which contains complex information after variational mode decomposition (VMD), further combining with a kernel extreme learning machine (KELM) optimized by particle swarm optimization (PSO) to construct the VMD-RES.-EEMD-PSO-KELM model. In order to verify the validity of the model, this paper conducts empirical analyses of Brent crude oil and West Texas Intermediate (WTI) crude oil. The empirical results show that the prediction model proposed in this paper improves the prediction accuracy of crude oil prices.

Keywords: Crude oil price forecasting; Two-layer decomposition technique; Extreme learning machine (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (20)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:229:y:2021:i:c:s0360544221010458

DOI: 10.1016/j.energy.2021.120797

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