期刊
NEUROCOMPUTING
卷 309, 期 -, 页码 70-82出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2018.04.071
关键词
Forecasting neural network model; Global energy fluctuation; Stochastic recurrent wavelet neural network; Discrete wavelet transform; Multi-scale composite complexity synchronization; Prediction accuracy estimate
资金
- National Natural Science Foundation of China [71271026]
Forecasting the fluctuations of global energy markets has become a focus of economic and energy research. In this paper, in an attempt to improve the prediction accuracy of energy prices, a novel hybrid neural network is developed through combining discrete wavelet transform (DWT) and stochastic recurrent wavelet neural network (SRWNN). The DWT is utilized as a processing technique to decompose subseries with different frequency, and the SRWNN model is established based on the randomization of wavelet neural network (WNN), which considers the memory of historical events and the weights of historical data depending on their occurrence time. The empirical experiments are performed in the prediction of four energy market prices, and the effectiveness of proposed DWT-SRWNN model is presented through contrastive results of the different predictive models. Further, a novel approach called multi-scale composite complexity synchronization (MCCS) is applied to display and evaluate the predictive effect. The empirical results demonstrate a higher accuracy of the proposed hybrid model in global energy price series forecasting. (C) 2018 Elsevier B.V. All rights reserved.
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