Forecasting wavelet neural hybrid network with financial ensemble empirical mode decomposition and MCID evaluation

By considering the properties of nonlinear data and the impact of historical data, this paper combines ensemble empirical mode decomposition (EEMD) into wavelet neural network with random time effective (WNNRT) to establish a hybrid neural network prediction model to improve the prediction accuracy of energy prices The EEMD is a noise-aided data analyze method, since it can effectively suppress pattern confusion and restore signal essence. Different from traditional models, the random time effective function that considers the timeliness of historical data and the random change of market environment is applied to the wavelet neural network to establish the WNNRT model. Moreover, multiscale complexity invariant distance (MCID) is utilized to evaluate the predicting performance of EEMD-WNNRT model. Further, the proposed model which is tested in predicting the impact on the global energy prices has carried on the empirical research, and it has also proved the corresponding superiority.

Automated summary

This paper combines ensemble empirical mode decomposition and wavelet neural network with random time effective function to establish a hybrid neural network prediction model, improving the prediction accuracy of energy prices. By utilizing multiscale complexity invariant distance to evaluate the predicting performance of the model, it has been proven superior in predicting the impact on global energy prices.