CPPCNDL: Crude oil price prediction using complex network and deep learning algorithms

作     者：Makumbonori Bristone Rajesh Prasad Adamu Ali Abubakar 

作者机构：School of IT and ComputingAmerican University of NigeriaYolaAdamawa StateNigeria Department of Computer Science and EngineeringAfrican University of Science and TechnologyAbujaNigeria 

出 版 物：《Petroleum》 (油气（英文）)

年 卷 期：2020年第6卷第4期

页      面：353-361页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020205[经济学-产业经济学] 

主　　题：Complex network analysis Deep learning Long-short term memory network K-core centrality Artificial intelligence Crude oil price prediction 

摘      要：Crude oil price prediction is a challenging task in oil producing *** price is among the most complex and tough to model because fluctuations of price of crude oil are highly irregular,nonlinear and varies dynamically with high *** paper proposed a hybrid model for crude oil price prediction that uses the complex network analysis and long short-term memory(LSTM)of the deep learning *** complex network analysis tool called the visibility graph is used to map the dataset on a network and K-core centrality was employed to extract the non-linearity features of crude oil and reconstruct the *** complex network analysis is carried out in order to preprocess the original data to extract the non-linearity features and to reconstruct the ***,LSTM was employed to model the reconstructed *** verify the result,we compared the empirical results with other research in the *** experiments show that the proposed model has higher accuracy,and is more robust and reliable.