A hybrid technique for dinar coin price prediction using artificial neural network based autogressive modeling technique

The recent introduction of Islamic Gold (Dinar) and Silver (Dirham) coins around the world has brought about a new paradigm in the world financial, economic and monetary system. The importance of accurately predicting the price of these coins ahead of time will contribute significantly to its usage...

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Main Authors:	Aibinu, Abiodun Musa, Salami, Momoh Jimoh Emiyoka, Ameer Amsa, Mohamad Ghazali
Format:	Conference or Workshop Item
Language:	English
Published:	2011
Subjects:	HB221 Price
Online Access:	http://irep.iium.edu.my/1767/ http://irep.iium.edu.my/1767/ http://irep.iium.edu.my/1767/1/A_hybrid_technique_for_dinar.pdf

id	iium-1767
recordtype	eprints
spelling	iium-17672012-08-01T02:04:39Z http://irep.iium.edu.my/1767/ A hybrid technique for dinar coin price prediction using artificial neural network based autogressive modeling technique Aibinu, Abiodun Musa Salami, Momoh Jimoh Emiyoka Ameer Amsa, Mohamad Ghazali HB221 Price The recent introduction of Islamic Gold (Dinar) and Silver (Dirham) coins around the world has brought about a new paradigm in the world financial, economic and monetary system. The importance of accurately predicting the price of these coins ahead of time will contribute significantly to its usage for: daily transaction, investment and development of necessary infrastructures for the universal adoption of these coins. Thus in this work, recently proposed artificial neural network based autoregressive (ANN-BASED AR) modeling technique has been applied in predicting accurately the daily price of Islamic Dinar coin. The input data is formatted to meet the input data requirement of the ANN-based AR model. The formatted data are then fed to the ANN-based AR model for parameters estimation. Upon convergence, the required model coefficients are computed from the synaptic weights and adaptive coefficients of the activated function in a two layer feed forward back-propagation artificial neural network (ANN) system. Performance analysis of the proposed approach shows that this proposed hybrid technique can accurately predict the price of Dinar coin and the use of this approach shows better performance when compared to the use of linear prediction technique. Other likely areas of application of the proposed approach have also been presented in this paper. 2011 Conference or Workshop Item NonPeerReviewed application/pdf en http://irep.iium.edu.my/1767/1/A_hybrid_technique_for_dinar.pdf Aibinu, Abiodun Musa and Salami, Momoh Jimoh Emiyoka and Ameer Amsa, Mohamad Ghazali (2011) A hybrid technique for dinar coin price prediction using artificial neural network based autogressive modeling technique. In: 2nd World Conference on Riba: The Riba Conundrum: Impartial Outlook from Accounting and Religious Perspectives, 26-27 July 2011, Putra World Trade Centre (PWTC), Kuala Lumpur. http://www.worldribaconference.org/about.html
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	HB221 Price
spellingShingle	HB221 Price Aibinu, Abiodun Musa Salami, Momoh Jimoh Emiyoka Ameer Amsa, Mohamad Ghazali A hybrid technique for dinar coin price prediction using artificial neural network based autogressive modeling technique
description	The recent introduction of Islamic Gold (Dinar) and Silver (Dirham) coins around the world has brought about a new paradigm in the world financial, economic and monetary system. The importance of accurately predicting the price of these coins ahead of time will contribute significantly to its usage for: daily transaction, investment and development of necessary infrastructures for the universal adoption of these coins. Thus in this work, recently proposed artificial neural network based autoregressive (ANN-BASED AR) modeling technique has been applied in predicting accurately the daily price of Islamic Dinar coin. The input data is formatted to meet the input data requirement of the ANN-based AR model. The formatted data are then fed to the ANN-based AR model for parameters estimation. Upon convergence, the required model coefficients are computed from the synaptic weights and adaptive coefficients of the activated function in a two layer feed forward back-propagation artificial neural network (ANN) system. Performance analysis of the proposed approach shows that this proposed hybrid technique can accurately predict the price of Dinar coin and the use of this approach shows better performance when compared to the use of linear prediction technique. Other likely areas of application of the proposed approach have also been presented in this paper.
format	Conference or Workshop Item
author	Aibinu, Abiodun Musa Salami, Momoh Jimoh Emiyoka Ameer Amsa, Mohamad Ghazali
author_facet	Aibinu, Abiodun Musa Salami, Momoh Jimoh Emiyoka Ameer Amsa, Mohamad Ghazali
author_sort	Aibinu, Abiodun Musa
title	A hybrid technique for dinar coin price prediction using artificial neural network based autogressive modeling technique
title_short	A hybrid technique for dinar coin price prediction using artificial neural network based autogressive modeling technique
title_full	A hybrid technique for dinar coin price prediction using artificial neural network based autogressive modeling technique
title_fullStr	A hybrid technique for dinar coin price prediction using artificial neural network based autogressive modeling technique
title_full_unstemmed	A hybrid technique for dinar coin price prediction using artificial neural network based autogressive modeling technique
title_sort	hybrid technique for dinar coin price prediction using artificial neural network based autogressive modeling technique
publishDate	2011
url	http://irep.iium.edu.my/1767/ http://irep.iium.edu.my/1767/ http://irep.iium.edu.my/1767/1/A_hybrid_technique_for_dinar.pdf
first_indexed	2023-09-18T20:09:16Z
last_indexed	2023-09-18T20:09:16Z
_version_	1777407367354253312

A hybrid technique for dinar coin price prediction using artificial neural network based autogressive modeling technique

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