Artificial Intelligence-Based Prediction of Crude Oil Prices Using Multiple Features under the Effect of Russia–Ukraine War and COVID-19 Pandemic

Hadi Jahanshahi (), Süleyman Uzun, Sezgin Kaçar, Qijia Yao and Madini O. Alassafi
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Hadi Jahanshahi: Department of Mechanical Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
Süleyman Uzun: Computer Engineering Department, Technology Faculty, Sakarya University of Applied Sciences, Sakarya 54050, Turkey
Sezgin Kaçar: Electrical and Electronics Engineering Department, Technology Faculty, Sakarya University of Applied Sciences, Sakarya 54050, Turkey
Qijia Yao: School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China
Madini O. Alassafi: Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Mathematics, 2022, vol. 10, issue 22, 1-14

Abstract: The effect of the COVID-19 pandemic on crude oil prices just faded; at this moment, the Russia–Ukraine war brought a new crisis. In this paper, a new application is developed that predicts the change in crude oil prices by incorporating these two global effects. Unlike most existing studies, this work uses a dataset that involves data collected over twenty-two years and contains seven different features, such as crude oil opening, closing, intraday highest value, and intraday lowest value. This work applies cross-validation to predict the crude oil prices by using machine learning algorithms (support vector machine, linear regression, and rain forest) and deep learning algorithms (long short-term memory and bidirectional long short-term memory). The results obtained by machine learning and deep learning algorithms are compared. Lastly, the high-performance estimation can be achieved in this work with the average mean absolute error value over 0.3786.

Keywords: prediction of crude oil prices; COVID-19 effect; Russia–Ukraine war effect; machine learning; deep learning; time series forecasting (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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