The crude oil price has been subject to periodic fluctuations because of seasonal changes in industrial demand and supply, weather, natural disasters and global political unrest. An accurate forecast of crude oil prices is of utmost importance for decision makers and industry players in the energy sector. Despite this, the volatility of crude oil prices contributes to the uncertainty of the energy industry, which was particularly challenging following the recent global spread of the COVID-19 epidemic and the Russia–Ukraine conflict. This paper proposes a hybrid deep learning (DL) modelling framework to deal with the volatility of crude oil prices, applying ensemble empirical mode decomposition (EEMD), convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) integrated with quantile regression (QR); named EEMD-CNN-BiLSTM-QR. Two real-world datasets on crude oil prices from the West Texas Intermediate and Brent Crude Oil markets were employed to validate the EEMD-CNN-BiLSTM-QR hybrid modelling framework. Given that the probability density forecast can capture uncertainty, an in-depth analysis was carried out and prediction accuracy calculated. The findings of this study demonstrate that the proposed EEMD-CNN-BiLSTM-QR DL modelling framework, which uses the probability density forecast method, is superior to other tested models in terms of its ability to forecast crude oil prices. The novelty of this study stems mainly from its use of QR, which allows for the description of the conditional distribution of predicted variables and the extraction of more uncertain information for probability density forecasts.

中文翻译：

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智能原油价格概率预测：深度学习模型及行业应用


由于工业供需季节性变化、天气、自然灾害和全球政治动荡等因素，原油价格出现周期性波动。准确预测原油价格对于能源领域的决策者和行业参与者至关重要。尽管如此，原油价格的波动加剧了能源行业的不确定性，尤其是在最近新冠肺炎（COVID-19）疫情全球蔓延和俄罗斯-乌克兰冲突之后，能源行业面临的挑战尤其严峻。本文提出了一种混合深度学习（DL）建模框架来处理原油价格的波动，应用集成经验模式分解（EEMD）、卷积神经网络（CNN）和与分位数集成的双向长短期记忆（BiLSTM）回归（QR）；命名为 EEMD-CNN-BiLSTM-QR。使用来自西德克萨斯中质油和布伦特原油市场的两个真实原油价格数据集来验证 EEMD-CNN-BiLSTM-QR 混合建模框架。鉴于概率密度预测可以捕捉不确定性，进行了深入分析并计算了预测精度。本研究的结果表明，所提出的 EEMD-CNN-BiLSTM-QR DL 建模框架采用概率密度预测方法，在预测原油价格的能力方面优于其他测试模型。这项研究的新颖性主要源于它使用QR，它可以描述预测变量的条件分布并提取更多不确定信息进行概率密度预测。