[Yang, Hua] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.^[Zhang, Yunfei;Jiang, Feng] Zhongnan Univ Econ & Law, Sch Stat & Math, Wuhan 430073, Peoples R China.
会议赞助商：
Chinese Association of Automation (CAA); Guangdong University of Technology; Systems Engineering Society of China (SESC); Technical Committee on Control Theory (TCCT), Chinese Association of Automation (CAA)
Foundation of WHPU [2018Y21]; National Natural Science Foundation of China [61773401, 61304067, 11601524]; Foundation of Hubei Province of China [17G024, 2017132]
机构署名：
本校为第一机构
院系归属：
数学与计算机学院
摘要：
The frequent fluctuations in international crude oil prices may affect the stability of the global economy and society. The fluctuation of crude oil prices has nonlinearity, uncertainty and volatility, which bring certain challenges for forecasting crude oil prices. In this paper we use hybrid model with the empirical mode decomposition (EMD) and Back Propagation Neural Network (BPNN) to predict the crude oil prices. To improve the accuracy of prediction, we firstly decompose the crude oil prices data into a series of independent intrinsic mode functions (IMFs) and residual sequences by the em...
