A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms. (15th June 2018)

• Record Type: Journal Article
• Authors: Wang, Minggang; Zhao, Longfeng; Du, Ruijin; Wang, Chao; Chen, Lin; Tian, Lixin; Eugene Stanley, H.
• Is Part Of: Applied energy. Volume 220(2018)
• Page Start: 480
• Page End: 495
• Publication Date: 2018-06-15
• Subjects: Complex network -- Artificial intelligence algorithms -- Crude oil price prediction; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2018.03.148
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A novel prediction paradigm (DFN-AI) is proposed based on complex network and AI algorithms. DFN analysis technique is performed to extract the fluctuation features in original data. A new data reconstruction method is designed by using the extracted data. A certain artificial intelligence tool is employed to model the reconstructed data. Empirical results demonstrate the effectiveness and robustness of DFN-AI method. 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.