A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms

Minggang Wang, Longfeng Zhao, Ruijin Du, Chao Wang, Lin Chen, Lixin Tian and H. Eugene Stanley

Applied Energy, 2018, vol. 220, issue C, 480-495

Abstract: Forecasting the price of crude oil is a challenging task. To improve this forecasting, this paper proposes a novel hybrid method that uses an integrated data fluctuation network (DFN) and several artificial intelligence (AI) algorithms, named DFN-AI model. In the proposed DFN-AI model, a complex network time series analysis technique is performed as a preprocessor for the original data to extract the fluctuation features and reconstruct the original data, and then an artificial intelligence tool, e.g., BPNN, RBFNN or ELM, is employed to model the reconstructed data and predict the future data. To verify these results we examine the daily, weekly, and monthly price data from the crude oil trading hub in Cushing, Oklahoma. Empirical results demonstrate that the proposed DFN-AI models (i.e., DFN-BP, DFN-RBF, and DFN-ELM) perform significantly better than their corresponding single AI models in both the direction and level of prediction. This confirms the effectiveness of our proposed modeling of the nonlinear patterns hidden in crude oil prices. In addition, our proposed DFN-AI methods are robust and reliable and are unaffected by random sample selection, sample frequency, or breaks in sample structure.

Keywords: Complex network; Artificial intelligence algorithms; Crude oil price prediction (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (50)

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DOI: 10.1016/j.apenergy.2018.03.148

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