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...
| Main Authors: | , , , , , |
|---|---|
| Format: | Proceeding Paper |
| Language: | English |
| Published: |
2013
|
| Subjects: | |
| Online Access: | http://irep.iium.edu.my/35755/ http://irep.iium.edu.my/35755/1/06716714.pdf |
| _version_ | 1848781107503300608 |
|---|---|
| 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 |
| building | IIUM Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T15:44:18Z |
| format | Proceeding Paper |
| id | iium-35755 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T15:44:18Z |
| publishDate | 2013 |
| recordtype | eprints |
| repository_type | Digital Repository |
| 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 Proceeding Paper 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 |
| 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 |
| title | 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_short | 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 |
| topic | T Technology (General) |
| 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 |