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Forecasting global crude oil price fluctuation by novel hybrid E-STERNN model and EMCCS assessment

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Abstract

Energy futures are a very significant part of commodity futures, no less than the influence of the spot market. A novel hybrid neural network (denote by E-STERNN) is proposed through combining Elman recurrent neural network model with stochastic time strength (ST-ERNN), and ensemble empirical mode decomposition (EEMD) is also introduced to improve the performance of forecasting neural network system for energy markets. ST-ERNN model is established for taking into account the weight of energy historical data with time variations. EEMD is an algorithm that decomposes any non-stationary and nonlinear time series into simple and independent time sequence. From the empirical research for four global energy market prices, the proposed hybrid E-STERNN model is verified to have higher prediction accuracy compared with the original ERNN and the ST-ERNN models. Moreover, a new error evaluation approach, called the exponent of multi-scale composite complexity synchronization (EMCCS), is utilized to analyze and estimate the prediction performance, and the demonstration analyses confirm that the hybrid E-STERNN model has higher prediction accuracy for global energy futures indexes.

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Abbreviations

ERNN:

Elman recurrent neural network

ST-ERNN:

Elman recurrent neural network model with stochastic time strength

EEMD:

Ensemble empirical mode decomposition

EMD:

Empirical mode decomposition

ANN:

Artificial neural network

IMF:

Intrinsic mode functions

WTI:

West Texas Intermediate crude oil

Brent:

Brent crude oil

PTR (NYSE):

PetroChina Co. Ltd.

CEO (NYSE):

China National Offshore Oil Co. Ltd.

MCCS:

Multi-scale composite complexity synchronization

CID:

Complexity invariant distance

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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

    Lihong Zhang & Jun Wang

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  1. Lihong Zhang
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  2. Jun Wang
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Correspondence to Lihong Zhang or Jun Wang.

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Zhang, L., Wang, J. Forecasting global crude oil price fluctuation by novel hybrid E-STERNN model and EMCCS assessment. Soft Comput 25, 2647–2663 (2021). https://doi.org/10.1007/s00500-020-05327-3

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