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

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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. • The deficiency of the “decomposition-prediction” combination model is improved. • The model is constructed by introducing the two-layer decomposition technology. • The model is applied to the multi-step-ahead research.