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

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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.

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Abbreviations

WNN:

Wavelet neural network

SRWNN:

Stochastic recurrent wavelet neural network

E-SRWNN:

Stochastic recurrent wavelet neural network with ensemble empirical mode decomposition

BP:

Back-propagation

GRU:

Gated recurrent unit

EMD:

Empirical mode decomposition

EEMD:

Ensemble empirical mode decomposition

CEEMDAN:

Complete ensemble empirical mode decomposition with adaptive noise

IMFs:

Intrinsic mode functions

WTI:

West Texas Intermediate crude oil

BRE:

Brent crude oil

CEO:

CNOOC Limited

OXY:

Occidental Petroleum Corp

MAE:

Mean absolute error

MAPE:

Mean absolute percent error

RMSE:

Root mean square error

SMAPE:

Symmetric mean absolute percent error

TIC:

Theil inequality coefficient

R :

Correlation coefficient

CID:

Complexity-invariant distance

MCID:

Multiscale complexity-invariant distance

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Acknowledgements

Authors were supported by National Natural Science Foundation of China Grant No. 71271026.

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  1. School of Science, Beijing Jiaotong University, Beijing, 100044, People’s Republic of China

    Jingmiao Li & Jun Wang

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  1. Jingmiao Li
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  2. Jun Wang
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Correspondence to Jingmiao Li.

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Li, J., Wang, J. Stochastic recurrent wavelet neural network with EEMD method on energy price prediction. Soft Comput 24, 17133–17151 (2020). https://doi.org/10.1007/s00500-020-05007-2

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