Prophesying the Short-Term Dynamics of the Crude Oil Future Price by Adopting the Survival of the Fittest Principle of Improved Grey Optimization and Extreme Learning Machine

Mathematics

Hesham El-Sayed¹  Asit Kumar Das²  Debahuti Mishra²  Kaberi Das²  Sachin Kumar³  Mikhail Zymbler³  Pradeep Kumar Mallick⁴ 

[1] College of Information Technology, United Arab Emirates University, Abu Dhabi 15551, United Arab Emirates;Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan (Deemed to be) University, Bhuwaneshawar 751030, India;Department of Computer Science, South Ural State University, 454080 Chelyabinsk, Russia;School of Computer Engineering, KIIT Deemed to be University, Bhuwaneshawar 751024, India;

关键词: crude oil forecasting;    survival of the fittest (SOF);    extreme learning machine (ELM);    differential evolution (DE);    particle swarm optimization (PSO);    grey wolf optimizer (GWO);   

DOI  :  10.3390/math10071121

来源: DOAJ

【 摘 要 】

Crude oil market analysis has become one of the emerging financial markets and the volatility effect of the market is paramount and has been considered as an issue of utmost importance. This study examines the dynamics of this volatile market of crude oil by employing a hybrid approach based on an extreme learning machine (ELM) as a regressor and the improved grey wolf optimizer (IGWO) for prophesying the crude oil rate for West Texas Intermediate (WTI) and Brent crude oil datasets. The datasets are augmented using technical indicators (TIs) and statistical measures (SMs) to obtain better insight into the forecasting ability of this proposed model. The differential evolution (DE) strategy has been used for evolution and the survival of the fittest (SOF) principle has been used for elimination while implementing the GWO to achieve better convergence rate and accuracy. Whereas, the algorithmic simplicity, use of less parameters, and easy implementation of DE efficiently decide the evolutionary patterns of wolves in GWO and the SOF principle updates the wolf pack based on the fitness value of each wolf, thereby ensuring the algorithm does not fall into local optimum. Furthermore, the comparison and analysis of the proposed model with other models, such as ELM–DE, ELM–Particle Swarm Optimization (ELM–PSO), and ELM–GWO shows that the predictability evidence obtained substantially achieves better performance for ELM–IGWO with respect to faster error convergence rate and mean square error (MSE) during training and testing phases. The sensitivity study of the proposed ELM–IGWO provides better results in terms of the performance measures, such as Theil’s U, mean absolute error (MAE), average relative variance (ARV), mean average percentage error (MAPE), and minimal computational time.

【 授权许可】

Unknown