ItemConference Paper

Predicting Crude Oil Futures using Feed Forward Neural Networks and Technical Indicators: A Comparative Study on WTI and Brent

Title
Predicting Crude Oil Futures using Feed Forward Neural Networks and Technical Indicators: A Comparative Study on WTI and Brent
Creator
Kumar Chandar, S.; Punjabi, Hitesh; Malik, Mayur
Description
In the domains of economic management and energy analysis, forecasting the price of crude oil is increasing popularity. It is essential to the facilitating rapid and cost-effective development with improved quality. Accurate prediction of the crude oil market is essential for steady and fast economic development because of its enormous influence on the global economy and society. Moreover, precise crude oil price prediction aids the traders in making accurate decision to maximize profits. In this work, a machine learning method for forecasting future global price data for crude oil is provided based on past data. The proposed model consists of three phases: primarily, historical data of selected crude oil data are gathered and normalized using data normalization technique. Secondly, technical indicators are derived from the crude oil data. Finally, a Feed Forward Neural Network (FFNN) is designed and trained using these technical indicators to forecast the price of crude oil in the future. Daily, weekly, and monthly data from Brent crude oil and West Texas Intermediate (WTI) are used to evaluate the generated model's prediction ability. To find the most effective FFNN configuration, the model's efficacy is evaluated by adjusting hidden layer number and hidden neurons. Performance of the model is also analyzed by varying number of training and testing samples. The experimental outcomes demonstrates that the designed model exhibits excellent performance for both WTI and Brent data. Notably, the model proves to be effective in predicting crude oil prices, when technical indicators are used as input variables. 2026 IEEE.
Source
Proceedings of the 2026 International Conference on AI-Driven Smart Systems and Ubiquitous Computing, ICAUC 2026;pp.1602-1612
Date
01-01-2026
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
and Technical Indicators; Crude Oil Price Prediction; Feed Forward Neural Network; Machine Learning; Prediction Accuracy
Coverage
Kumar Chandar S., Christ University, School of Business and Management, India; Punjabi H., K J Somaiya Institute of Management, India; Malik M., International Open University, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833155851-2;
Format
online
Language
English
Type
Conference paper

Citation

Kumar Chandar, S.; Punjabi, Hitesh; Malik, Mayur, “Predicting Crude Oil Futures using Feed Forward Neural Networks and Technical Indicators: A Comparative Study on WTI and Brent,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25907.

Output Formats