Stochastic recurrent wavelet neural network with EEMD method on energy price prediction
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METHODOLOGIES AND APPLICATION
Stochastic recurrent wavelet neural network with EEMD method on energy price prediction Jingmiao Li1 · Jun Wang1
© Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract 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. Keywords Prediction · Stochastic recurrent wavelet neural network · Ensemble empirical mode decomposition · Energy indexes · Error evaluation · Multiscale complexity-invariant distance
Abbreviations WNN SRWNN E-SRWNN
BP GRU EMD EEMD CEEMDAN IMFs WTI BRE
Wavelet neural network Stochastic recurrent wavelet neural network Stochastic recurrent wavelet neural network with ensemble empirical mode decomposition Back-propagation Gated recurrent unit Empirical mode decomposition Ensemble empirical mode decomposition Complete ensemble empirical mode decomposition with adaptive noise Intrinsic mode functions West Texas Intermediate crude oil Brent crude oil
Communicated by V. Loia.
B 1
Jingmiao Li [email protected] School of Science, Beijing Jiaotong University, Beijing 100044, People’s Republic of China
CEO OXY MAE MAPE RMSE SMAPE TIC R CID MCID
CNOOC Limited Occidental Petroleum Corp Mean absolute error Mean absolute percent error Root mean square error Symmetric mean absolute percent error Theil inequality coefficient Correlation coefficient Complexity-invariant distance Multiscale complexity-invariant distance
1 Introduction Energy is a top priority for the sustainable development of any country’s society, economy and environment. In the past decade, the energy consumption index has been on the rise. In the 1980s, the world’s basic resources changed from coal to oil. Nowadays, it is fashionable to predict and analyze the future development trend of energy indexes.
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