当前位置: X-MOL 学术Energy › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
A new hybrid model for forecasting Brent crude oil price
Energy ( IF 9 ) Pub Date : 2020-06-01 , DOI: 10.1016/j.energy.2020.117520
Hooman Abdollahi , Seyed Babak Ebrahimi

Abstract Received a plethora of attention by both practitioners and researchers, oil price forecasting remains a challenging issue due to the particular characteristics of oil price and its prodigious impact on various economic sectors. Motivated by this issue, the authors aim to introduce a robust hybrid model for reliable forecasting of Brent oil price. For this purpose, the Adaptive Neuro Fuzzy Inference System (ANFIS), Autoregressive Fractionally Integrated Moving Average (ARFIMA), and Markov-switching models are employed in the proposed hybrid model. The cardinal merit of this hybridization lies in the fact that the constituent models are capable of capturing particular features like nonlinearity, lag, and market interrelationships existing in oil price time series. Then, specific weights are assigned to each model to achieve an accurate prediction of the empirical time series. Three weighting scenarios, namely equal weights, error-value-based weights, and genetic algorithm weighting function, are applied. The authors use root mean square error, mean absolute error, and mean absolute percentage error to measure errors. Robustness of results and prediction quality of the hybrid model compared with counterparts are also investigated by Diebold-Mariano test. Finally, numerical results reveal that the hybrid model weighted by genetic algorithm generally outperforms the constituent models, hybrid model with equal weights, and hybrid model weighted based on the error values. Reliable forecasting of crude oil prices is especially beneficial to producer and importer nations to optimize their production and order rates and mitigate the adverse effect of possible shocks.

中文翻译:

预测布伦特原油价格的新混合模型

摘要 由于油价的特殊性及其对各个经济部门的巨大影响,油价预测受到了从业者和研究人员的广泛关注,仍然是一个具有挑战性的问题。受此问题的启发,作者旨在引入一种稳健的混合模型,以可靠地预测布伦特原油价格。为此,在提议的混合模型中采用了自适应神经模糊推理系统 (ANFIS)、自回归分数积分移动平均 (ARFIMA) 和马尔可夫切换模型。这种混合的主要优点在于,组成模型能够捕捉石油价格时间序列中存在的非线性、滞后和市场相互关系等特定特征。然后,为每个模型分配特定的权重,以实现对经验时间序列的准确预测。应用了三种加权场景,即等权重、基于误差值的权重和遗传算法加权函数。作者使用均方根误差、平均绝对误差和平均绝对百分比误差来衡量误差。与对应模型相比,混合模型的结果稳健性和预测质量也通过 Diebold-Mariano 检验进行了研究。最后,数值结果表明,遗传算法加权的混合模型总体上优于组成模型、等权重的混合模型和基于误差值加权的混合模型。
更新日期:2020-06-01
down
wechat
bug