Co - active neuro-fuzzy inference systems model for predicting crude oil price based on OECD inventories

Co - active neuro-fuzzy inference systems model for predicting crude oil price based on OECD inventories

This paper present a novel approach to crude oil price prediction based on co-active neuro-fuzzy inference systems (CANFIS) instead of the commonly use fuzzy neural network and adaptive network-based fuzzy inference systems due to superiority and robustness of the CANFIS model. Monthly data of West...

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Main Authors: Chiroma, Haruna, Abdulkareem, Sameem, Abubakar, Adamu, Zeki, Akram M., Gital, Abdulsam Ya'u, Usman, Mohammed Joda
Format: Conference or Workshop Item
Language:English
Published: 2013
Subjects:
T Technology (General)
Online Access:http://irep.iium.edu.my/35755/
http://irep.iium.edu.my/35755/
http://irep.iium.edu.my/35755/
http://irep.iium.edu.my/35755/1/06716714.pdf
id iium-35755
recordtype eprints
spelling iium-357552014-12-08T03:47:18Z http://irep.iium.edu.my/35755/ Co - active neuro-fuzzy inference systems model for predicting crude oil price based on OECD inventories Chiroma, Haruna Abdulkareem, Sameem Abubakar, Adamu Zeki, Akram M. Gital, Abdulsam Ya'u Usman, Mohammed Joda T Technology (General) This paper present a novel approach to crude oil price prediction based on co-active neuro-fuzzy inference systems (CANFIS) instead of the commonly use fuzzy neural network and adaptive network-based fuzzy inference systems due to superiority and robustness of the CANFIS model. Monthly data of West Texas Intermediate crude oil price and organization for economic co-operation and development (OECD) inventories, obtained from US Department of Energy were used to built the propose model. The CANFIS prediction model was trained, validated and tested. The performance of our approach is measured using mean square error, root mean square error, mean absolute error and regression. Suggestion from the results shows that the CANFIS demonstrated a high level of generalization capability with relatively very low error and high correlation which exhibited successful prediction performance of the proposal. The model has the potential of being developed into real life systems for use by both government and private businesses for making strategic planning that can boost economic activities. 2013 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/35755/1/06716714.pdf Chiroma, Haruna and Abdulkareem, Sameem and Abubakar, Adamu and Zeki, Akram M. and Gital, Abdulsam Ya'u and Usman, Mohammed Joda (2013) Co - active neuro-fuzzy inference systems model for predicting crude oil price based on OECD inventories. In: 3rd International Conference on Research and Innovation in Information Systems – 2013 (ICRIIS’13), 27 -28th November 2013, Uniten, Kajang. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6716714 doi:10.1109/ICRIIS.2013.6716714
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
topic T Technology (General)
spellingShingle T Technology (General)
Chiroma, Haruna
Abdulkareem, Sameem
Abubakar, Adamu
Zeki, Akram M.
Gital, Abdulsam Ya'u
Usman, Mohammed Joda
Co - active neuro-fuzzy inference systems model for predicting crude oil price based on OECD inventories
description This paper present a novel approach to crude oil price prediction based on co-active neuro-fuzzy inference systems (CANFIS) instead of the commonly use fuzzy neural network and adaptive network-based fuzzy inference systems due to superiority and robustness of the CANFIS model. Monthly data of West Texas Intermediate crude oil price and organization for economic co-operation and development (OECD) inventories, obtained from US Department of Energy were used to built the propose model. The CANFIS prediction model was trained, validated and tested. The performance of our approach is measured using mean square error, root mean square error, mean absolute error and regression. Suggestion from the results shows that the CANFIS demonstrated a high level of generalization capability with relatively very low error and high correlation which exhibited successful prediction performance of the proposal. The model has the potential of being developed into real life systems for use by both government and private businesses for making strategic planning that can boost economic activities.
format Conference or Workshop Item
author Chiroma, Haruna
Abdulkareem, Sameem
Abubakar, Adamu
Zeki, Akram M.
Gital, Abdulsam Ya'u
Usman, Mohammed Joda
author_facet Chiroma, Haruna
Abdulkareem, Sameem
Abubakar, Adamu
Zeki, Akram M.
Gital, Abdulsam Ya'u
Usman, Mohammed Joda
author_sort Chiroma, Haruna
title Co - active neuro-fuzzy inference systems model for predicting crude oil price based on OECD inventories
title_short Co - active neuro-fuzzy inference systems model for predicting crude oil price based on OECD inventories
title_full Co - active neuro-fuzzy inference systems model for predicting crude oil price based on OECD inventories
title_fullStr Co - active neuro-fuzzy inference systems model for predicting crude oil price based on OECD inventories
title_full_unstemmed Co - active neuro-fuzzy inference systems model for predicting crude oil price based on OECD inventories
title_sort co - active neuro-fuzzy inference systems model for predicting crude oil price based on oecd inventories
publishDate 2013
url http://irep.iium.edu.my/35755/
http://irep.iium.edu.my/35755/
http://irep.iium.edu.my/35755/
http://irep.iium.edu.my/35755/1/06716714.pdf
first_indexed 2023-09-18T20:51:14Z
last_indexed 2023-09-18T20:51:14Z
_version_ 1777410008153063424

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