Stochastic recurrent wavelet neural network with EEMD method on energy price prediction

Novel hybrid neural network prediction model (denoted by E-SRWNN) is formed by combining ensemble empirical mode decomposition (EEMD) and stochastic recurrent wavelet neural network (SRWNN), in order to improve the precision of energy indexes price forecasting. Energy index price series are non-stationary, nonlinear and random. EEMD method is utilized to decompose the closing prices of four energy indexes into subsequences with different frequencies, and the SRWNN model is composed by adding stochastic time effective function and recurrent layer to the wavelet neural network (WNN). Stochastic time effective function makes the model assign different weights to the historical data at different times, and the introduction of recurrent layer structure will enhance the data learning. In this paper, E-SRWNN model is compared with other WNN-based models and the deep learning network GRU. In the error evaluation, the general standards, such as linear regression analysis, mean absolute error and theil inequality coefficient, are utilized to compare the predicted effects of different models, and then multiscale complexity-invariant distance is applied for further analysis. Empirical research illustrates that the proposed E-SRWNN model displays strong forecasting ability and accurate forecasting results in energy price series forecasting.