Multi-source Model of Heterogeneous Data Analysis for Oil Price Forecasting

Authors

  • Pavel Baboshkin Financial University under the Government of the Russian Federation, Moscow, Russia
  • Mafura Uandykova Narxoz Uniiversity, Almaty, Republic of Kazakhstan

Abstract

This article sheds light on the question of whether it is possible to create fairly accurate forecasts of real oil prices. For this purpose, a multi-level machine learning model has been created to analyze several sources of heterogeneous data to predict future prices. The article uses different types of data: market condition data, titles, and transaction data. Then, they have been processed to be able to load them into the model. The validation of the regression neural network results showed that the model is more accurate than in previous studies. In fact, this paper presents an artificial neural network model that solves the problem of determining the most informative relationship between different types of oil price data. Keywords: artificial neural network, oil forecasting, machine learning, price prediction, energy resources.JEL Classifications: C45, C51, Q43, Q47DOI: https://doi.org/10.32479/ijeep.10853

Downloads

Download data is not yet available.

Downloads

Published

2021-02-01

How to Cite

Baboshkin, P., & Uandykova, M. (2021). Multi-source Model of Heterogeneous Data Analysis for Oil Price Forecasting. International Journal of Energy Economics and Policy, 11(2), 384–391. Retrieved from https://mail.econjournals.com/index.php/ijeep/article/view/10853

Issue

Vol. 11 No. 2 (2021)

Section

Articles

License

Submission of an article implies that the work described has not been published previously , that it is not under consideration for publication elsewhere, that its publication is approved by all authors and tacitly or explicitly by the responsible authorities where the work was carried out, and that, if accepted, will not be published elsewhere in the same form, without the written consent of the Publisher. The Editors reserve the right to edit or otherwise alter all contributions, but authors will receive proofs for approval before publication.