Machine Learning Insights into Predicting Crude Oil Prices

Title

Machine Learning Insights into Predicting Crude Oil Prices

Creator

Toppo A.; Mahajan J.; Singh V.P.; Paswan A.S.; Saxena A.

Description

This research paper delves into the complexities of crude oil, highlighting its extraction, composition, and transformation into valuable derivatives. Examining the pricing dynamics, it explores the intricate interplay of social and economic factors that shape crude oil's value, emphasizing its critical role in global energy and industrial sectors. A forecasting model is introduced, focusing on key factors - heating oil, SPX, GPNY, and EU DOL EX - utilizing five machine learning models. Historical data reveals the efficacy of conventional models, particularly Random Forest, in predicting crude oil prices, enhanced by feature engineering techniques. The paper concludes by suggesting avenues for further exploration, offering valuable insights for readers in this dynamic research domain. 2024 IEEE.

Source

TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024

Date

2024-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Crude Oil; Data Analysis; Feature Engineering; Machine Learning Algorithms; Model Evaluation; Predictive Analysis; Random Forest; Support Vector Machine

Coverage

Toppo A., Christ University, Bengaluru, India; Mahajan J., Christ University, Bengaluru, India; Singh V.P., Ewec, United Arab Emirates; Paswan A.S., Christ University, Bengaluru, India; Saxena A., Christ University, Bengaluru, India

Rights

Restricted Access

Relation

ISBN: 979-835038427-7

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/TQCEBT59414.2024.10545299

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204464958&doi=10.1109%2fTQCEBT59414.2024.10545299&partnerID=40&md5=d51df646f57bffb56e148bf80810ba6a