School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China.
Math Biosci Eng. 2021 Sep 17;18(6):8096-8122. doi: 10.3934/mbe.2021402.
In view of the important position of crude oil in the national economy and its contribution to various economic sectors, crude oil price and volatility prediction have become an increasingly hot issue that is concerned by practitioners and researchers. In this paper, a new hybrid forecasting model based on variational mode decomposition (VMD) and kernel extreme learning machine (KELM) is proposed to forecast the daily prices and 7-day volatility of Brent and WTI crude oil. The KELM has the advantage of less time consuming and lower parameter-sensitivity, thus showing fine prediction ability. The effectiveness of VMD-KELM model is verified by a comparative study with other hybrid models and their single models. Except various commonly used evaluation criteria, a recently-developed multi-scale composite complexity synchronization (MCCS) statistic is also utilized to evaluate the synchrony degree between the predictive and the actual values. The empirical results verify that 1) KELM model holds better performance than ELM and BP in crude oil and volatility forecasting; 2) VMD-based model outperforms the EEMD-based model; 3) The developed VMD-KELM model exhibits great superiority compared with other popular models not only for crude oil price, but also for volatility prediction.
鉴于原油在国民经济中的重要地位及其对各经济部门的贡献，原油价格及其波动预测已成为从业者和研究人员日益关注的热点问题。本文提出了一种基于变分模态分解（VMD）和核极限学习机（KELM）的新混合预测模型，用于预测布伦特和 WTI 原油的日价格和 7 天波动率。KELM 具有耗时少、参数敏感性低的优点，因此具有良好的预测能力。通过与其他混合模型及其单一模型的比较研究，验证了 VMD-KELM 模型的有效性。除了各种常用的评价标准外，还利用了最近开发的多尺度复合复杂度同步（MCCS）统计量来评估预测值与实际值之间的同步程度。实证结果验证了：1）KELM 模型在原油和波动率预测方面的表现优于 ELM 和 BP；2）基于 VMD 的模型优于基于 EEMD 的模型；3）所提出的 VMD-KELM 模型在原油价格和波动率预测方面均优于其他流行模型，具有很大的优势。
