Forecasting energy fluctuation model by wavelet decomposition and stochastic recurrent wavelet neural network

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.