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

By Minggang Wang, Longfeng Zhao, Ruijin Du, Chao Wang, Lin Chen, Lixin Tian, H. Eugene Stanley, Eugene Stanley. 2018

133 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Ruijin Du
2. Minggang Wang
3. H. Eugene Stanley
4. Lin Chen
5. Chao Wang
6. Zhilin Qiao
7. N. Rafizadeh
8. Hamed Ghoddusi

In Management Science and Operations Research

1. N. Rafizadeh
2. Hamed Ghoddusi
3. H. Eugene Stanley
4. Germán G. Creamer

Citations in the corpus

• Which Artificial Intelligence Algorithm Better Predicts the Chinese Stock Market? (2018, methodology): “Artificial intelligence algorithms have been used to predict economic and market behavior [32]–[34].”
• Machine learning in energy economics and finance: A review (2019, background): “Huang and Wang (2018) combines WNN with a random time effective function.”