Enhancing Crude Oil Price Prediction with Neural Network Models

Title

Enhancing Crude Oil Price Prediction with Neural Network Models

Creator

Punjabi, Hitesh; Kumar Chandar, S.; Malik, Mayur

Description

A nations economic, social, and national security are all severely affected by variations in crude oil prices, which is a basic energy source. Research on accurately forecasting price changes for crude oil is always progressing. This research presents a forecasting strategy for crude oil pricing using artificial neural networks. The presented model uses standardization techniques to prepare the historical data for the subsequent processes. It is possible to predict future prices by using a Feed Forward Neural Network (FFNN) with four layers. West Texas Intermediate (WTI) and Brent crude oil prices are utilized on a daily, weekly, and monthly basis to demonstration and confirmation. Directional statistic, accuracy of prediction, the model is evaluated using root mean square error and mean absolute error expressed as percentages. Empirical findings confirm that the suggested approach performs better than any of the previous approaches. Additionally, it is noted that the presented method achieved higher prediction in contrast to other methods. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Source

Lecture Notes in Networks and Systems;Volume;1755 LNNS;pp.30-44

Date

01-01-2026

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Artificial neural network; crude oil price; prediction; standardization

Coverage

Punjabi H., K J Somaiya Institute of Management, Mumbai, India; Kumar Chandar S., School of Business and Management, Christ University, Bengaluru, India; Malik M., International Open University, Banjul, Gambia

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-303213176-8;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-3-032-13177-5_3

https://www.scopus.com/pages/publications/105028779457?origin=resultslist