Open AccessCrude Oil Price Prediction Based on Soft Computing Model: Case Study of Iraq
Author(s) -
Saad Hassan Ali,
Abdullah Hasan Ali
Publication year - 2019
Publication title -
xi'nan jiaotong daxue xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.308
H-Index - 21
ISSN - 0258-2724
DOI - 10.35741/issn.0258-2724.54.4.36
Subject(s) - crude oil , artificial neural network , multilayer perceptron , soft computing , perceptron , computer science , oil price , econometrics , petroleum engineering , artificial intelligence , engineering , economics , monetary economics
The prediction of the price of crude oil is important for economic, political, and industrial purposes in both crude oil importing and exporting countries. Fluctuations in oil prices can have a significant influence in many countries. Therefore, it is necessary to develop a suitable model that can accurately predict different economic and engineering parameters that are directly related to the price of crude oil. This paper proposes the use of a soft computing (SC) model which consists of a multi-layer perceptron neural network (MLP-NN) for accurate predictions of future crude oil prices. The performance of the SC model proposed in this study was compared to that of other neural network approaches and found to perform better in the prediction of both monthly and daily crude oil prices, especially where there is a limited number of input data for model training and in situations of high parameter variability.