Predicting Oil Prices: A Comparative Study of Machine Learning and Deep Learning Methods

Abstract

Crude oil, often referred to as "black gold," is an essential component of the global economy due to its significant contribution to the world's energy requirements. Crude oil has been an essential energy source for the global economy. As numerous countries, regardless of their development status, rely significantly on its importation for transportation, heating, power generation, industrial production, agricultural output, and other applications. Energy security for all stakeholders, particularly oil-importing nations, will invariably rely on the sustained flow and provision of crude oil at competitive prices. The prices of crude oil substantially influence governmental decision-making. Consequently, variations in oil prices can incite social unrest and economic turmoil. Significantly impacting the global economy. Consequently, there is an increasing demand for methodologies that can accurately and efficiently forecast the future behavior of oil prices, considering their significant influence on the economy. To accurately predict oil prices, this study will make use of machine learning techniques such as Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Random Forest (RF), and Decision Tree (DT). Furthermore.  long short-term memory network (LSTM) method as deep learning model is also used. The incapacity of  recurrent neural networks (RNNs) to recognize long-range dependencies between distant data instances was a major drawback that LSTM was created to overcome. Despite its simplicity, the LSTM framework has remarkable performance across a wide range of applications. The LSTM models achieve a 99.99% prediction accuracy, outperforming other methods.