Efficient Crude Oil Pricing Using a Machine Learning Approach

O. Falode, C. Udomboso
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Abstract

Crude oil, a base for more than 6000 products that we use on a daily basis, accounts for 33% of global energy consumption. However, the outbreak and transmission of COVID-19 had significant implications for the entire value chain in the oil industry. The price crash and the fluctuations in price is known to have far reaching effect on global economies, with Nigeria hard. It has therefore become imperative to develop a tool for forecasting the price of crude oil in order to minimise the risks associated with volatility in oil prices and also be able to do proper planning. Hence, this article proposed a hybrid forecasting model involving a classical and machine learning techniques – autoregressive neural network, in determining the prices of crude oil. The monthly data used were obtained from the Central Bank of Nigeria website, spanning January 2006 to October 2020. Statistical efficiency was computed for the hybrid, and the models from which the proposed hybrid was built, using the percent relative efficiency. Analyses showed that the efficiency of the hybrid model, at 20 and 100 hidden neurons, was higher than that of the individual models, the latter being the best performing. The study recommends urgent diversification of the economy in order not for the nation to be plunged into a seemingly unending recession.
使用机器学习方法的高效原油定价
原油是我们每天使用的6000多种产品的基础,占全球能源消耗的33%。然而,COVID-19的爆发和传播对石油行业的整个价值链产生了重大影响。众所周知,油价暴跌和价格波动对全球经济产生了深远的影响,尼日利亚首当其冲。因此,开发一种预测原油价格的工具变得势在必行,以最大限度地降低与油价波动相关的风险,并能够进行适当的规划。因此,本文提出了一种混合预测模型,涉及经典和机器学习技术-自回归神经网络,以确定原油价格。使用的月度数据来自尼日利亚中央银行网站,时间跨度为2006年1月至2020年10月。计算了混合动力车的统计效率,并利用相对效率百分比建立了混合动力车的模型。分析表明,在20和100个隐藏神经元时,混合模型的效率高于单个模型,后者表现最好。该研究建议,为了不让国家陷入看似无休止的经济衰退,应尽快实现经济多元化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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