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Forecasting hybrid neural network with variational learning rate and q-DSCID synchronization evaluation for energy market

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Abstract

Because of the nonlinearity, uncertainty, and dynamics of crude oil price, its price forecasting has continuously been a burdensome international research issue. To better implement the prediction of the energy market by machine learning algorithms, premeditating the influence factors of historical data in different periods on prediction consequence, random inheritance formula error correction algorithm is proposed in this work. The empirical wavelet transform and reconstruction are applied to extract data features simultaneously. A novel hybrid neural network model is constructed, which integrates empirical wavelet transform, Elman recurrent neural network, and random inheritance formula. Variational learning rate is proposed and used to ameliorate the selection of parameters for the network training procedure. In this paper, the proposed model is applied in crude oil futures price forecasting. Further, a variety of evaluation indicators are introduced to contrast and evaluate the predictions. An original representative synchronization evaluation arithmetic q-order dyadic scales complexity invariant distance is put forward and utilized. Demonstration results suggest that the proposed model has superior preciseness among comparison models.

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Abbreviations

VR:

Variational learning rate

EWT:

Empirical wavelet transform

RIF:

Random inheritance formula

ANN:

Artificial neural network

RNN:

Recurrent neural network

GRU:

Gated recurrent unit

ERNN:

Elman recurrent neural network

RIF-ERNN:

Random inheritance Elman recurrent neural network

EWT-RIF-ERNN:

Random inheritance Elman recurrent neural network with empirical wavelet transform

CID:

Complexity invariant distance

q-DSCID:

q-order dyadic scales complexity invariant distance

WTI:

West Texas intermediate crude oil

BRE:

Brent crude oil

PTR:

China Petroleum and Natural Gas Co., Ltd

SNP:

China Petrochemical Co., Ltd

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Acknowledgements

The 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, China

    Bin Wang & Jun Wang

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  1. Bin Wang
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  2. Jun Wang
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Correspondence to Bin Wang or Jun Wang.

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Wang, B., Wang, J. Forecasting hybrid neural network with variational learning rate and q-DSCID synchronization evaluation for energy market. Soft Comput 24, 16811–16828 (2020). https://doi.org/10.1007/s00500-020-04977-7

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