A Machine Learning Approach to Crude Oil Price Prediction Using Support Vector Machine (SVM)
subject: Crude oil; energy market; machine learning; macroeconomic variables; normalization; support vector machine
description: Crude oil is one of the most important energy sources, and fluctuations in its international prices affect all aspects of the economy. The price of crude oil is influenced by several variables, and the length of time that each component has an effect differs giving an increase in non-linear oil price features. Although it is a complex task, identifying the most essential factor influencing for precise predicting, crude oil prices are essential. Therefore, this study aims to employ a machine learning model to address the intricate relationships among different factors. Primarily, it gathers data regarding West Texas Intermediate (WTI) and Brent crude oil prices as well as macroeconomic variables. Secondly, the data is normalized to prepare it for further analysis. Finally, a crude oil prediction model is constructed using Support Vector Machine (SVM) to predict future international crude oil prices. The daily, weekly, and monthly prices are used to confirm the models efficacy developed using WTI and Brent oil. The models performance is also evaluated by incorporating various combinations of macroeconomic variables to find the most influential factor. Results from experiments show indicates the benchmark model was much exceeded by the developed model and performed very well in terms of prediction accuracy. The findings reveal that selecting the appropriate variables can greatly enhance prediction accuracy. This model has the potential to provide valuable insights for traders, investors, and energy-related enterprises, offering beneficial guidance for decision-making purposes. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
creator: Punjabi, Hitesh; Kumar Chandar, S.; Malik, Mayur
source: Lecture Notes in Networks and Systems;Volume;1755 LNNS;pp.509-526
publisher: Springer Science and Business Media Deutschland GmbH
date: 01-01-2026
rights: Restricted Access; Hardcopy may be available in the library
relation: ISSN: 23673370; ISBN: 978-303213176-8;
language: English
type: Conference paper
coverage: Punjabi H., Maharashtra, Mumbai, India; Kumar Chandar S., School of Business and Management, Christ University, Karnataka, Bengaluru, India; Malik M., Research Scholar, International Open University, Banjul, Gambia