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

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.

RUemo—The Classification Framework for Russia-Ukraine War-Related Societal Emotions on Twitter through Machine Learning

“…Existing literature such as [35][36][37][38][39] focused on the specific domain of study to assess the impact of the RUW on the energy, gas (crude oil), food, healthcare, and stock markets, respectively, whereas our work highlights the generalized societal emotions and views of the RUW. Moreover, by complementing prior studies such as [40,41] we have chosen Twitter for this study to provide an automatic emotion classification framework by combining, NLP, TL, and ML altogether.…”

“…The COVID-19 pandemic's impact on crude oil prices had barely begun to diminish when the RUW brought forth a fresh problem. In [36], a novel application was created that incorporated these two worldwide influences to forecast changes in crude oil prices. This study, unlike most others, made use of a dataset with information gathered over a period of 22 years and seven distinct characteristics, such as the opening and closing prices of crude oil and the greatest and lowest prices recorded for any given day.…”

“…Prior studies, such as [35,36,38], have concentrated on a particular area for their research work to analyze the RUW impacts on the energy, gas (crude oil), food, healthcare, and stock markets, respectively. Thus, to fill a gap, we found a generalized way of analyzing societal emotions and opinions for the RUW.…”

International Comparison on the Application of Information Technology in High School Mathematics Textbooks

“…Umar et al (2022) examine the influence of the conflict on the renewable energy, traditional energy, and metal markets, and suggest that the abnormal returns of the renewable energy sector increased markedly following the onset of the Russia-Ukraine conflict. Jahanshahi et al (2022) combine deep learning algorithms and machine learning to develop a novel program to forecast oil prices in the context of the Russia-Ukraine conflict and the COVID-19 pandemic. Steffen and Patt (2022) consider the Russia-Ukraine conflict as a possible milestone for European energy policies and provide evidence pertaining to how the conflict gradually changes public opinion.…”

Nonlinear Dynamics of a Quantum Cournot Duopoly with Bounded Rationality and Relative Profit Maximization

“…Nonlinear economic models contain many complex phenomena. If there is confusion in an economic model, especially in a duopoly game, it can cause chaos and unpredictability in the market, such as [1][2][3]. Delaying or avoiding bifurcation and chaos is essential.…”