Forecasting Malaysian exchange rate artificial neural network : 2010-2018 / Ikhwan Muzammil Amran

In todays fast paced global economy, the accuracy in forecasting the foreign exchange rate or predicting the trend is a critical key for any future business to come. The use of computational intelligence based techniques for forecasting has been proved to be successful for quite some time. This stud...

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Main Author:	Amran, Ikhwan Muzammil
Format:	Student Project
Language:	English
Published:	Faculty of Computer and Mathematical Sciences 2018
Subjects:	Neural networks (Computer science) Artificial immune systems. Immunocomputers
Online Access:	http://ir.uitm.edu.my/id/eprint/26562/ http://ir.uitm.edu.my/id/eprint/26562/1/Pbb_IKHWAN%20MUZAMMIL%20AMRAN%20CS%20R%2019_5.pdf

id	uitm-26562
recordtype	eprints
spelling	uitm-265622019-11-25T08:53:57Z http://ir.uitm.edu.my/id/eprint/26562/ Forecasting Malaysian exchange rate artificial neural network : 2010-2018 / Ikhwan Muzammil Amran Amran, Ikhwan Muzammil Neural networks (Computer science) Artificial immune systems. Immunocomputers In todays fast paced global economy, the accuracy in forecasting the foreign exchange rate or predicting the trend is a critical key for any future business to come. The use of computational intelligence based techniques for forecasting has been proved to be successful for quite some time. This study presents a computational advance for forecasting the Foreign Exchange Rate in Kuala Lumpur for Ringgit Malaysia against US Dollar. A neural network based model has been used in forecasting the days ahead of exchange rate. The aims of this research are to make a prediction of Foreign Exchange Rate in Kuala Lumpur for Ringgit Malaysia against US Dollar using artificial neural network and determine practicality of the model. The Alyuda NeuroIntelligence software was utilized to analyse and to predict the data. After the data has been processed and the structural network compared to each other, the network of 2-4-1 has been chosen by outperforming other network. This network selection criteria is based on Akaike Information Criterion (AIC) value which shows the lowest of them all. The training algorithm that applied is Quasi-Netwon based on the lowest recorded absolute training error. This study come out with the daily future index for 90 days ahead and the index are quite similar with the past historical exchange rate. Hence, it is believed that experimental results demonstrate that Artificial Neural Network based model can closely predict the future exchange rate. Faculty of Computer and Mathematical Sciences 2018 Student Project NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/26562/1/Pbb_IKHWAN%20MUZAMMIL%20AMRAN%20CS%20R%2019_5.pdf Amran, Ikhwan Muzammil (2018) Forecasting Malaysian exchange rate artificial neural network : 2010-2018 / Ikhwan Muzammil Amran. [Student Project] (Unpublished)
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
topic	Neural networks (Computer science) Artificial immune systems. Immunocomputers
spellingShingle	Neural networks (Computer science) Artificial immune systems. Immunocomputers Amran, Ikhwan Muzammil Forecasting Malaysian exchange rate artificial neural network : 2010-2018 / Ikhwan Muzammil Amran
description	In todays fast paced global economy, the accuracy in forecasting the foreign exchange rate or predicting the trend is a critical key for any future business to come. The use of computational intelligence based techniques for forecasting has been proved to be successful for quite some time. This study presents a computational advance for forecasting the Foreign Exchange Rate in Kuala Lumpur for Ringgit Malaysia against US Dollar. A neural network based model has been used in forecasting the days ahead of exchange rate. The aims of this research are to make a prediction of Foreign Exchange Rate in Kuala Lumpur for Ringgit Malaysia against US Dollar using artificial neural network and determine practicality of the model. The Alyuda NeuroIntelligence software was utilized to analyse and to predict the data. After the data has been processed and the structural network compared to each other, the network of 2-4-1 has been chosen by outperforming other network. This network selection criteria is based on Akaike Information Criterion (AIC) value which shows the lowest of them all. The training algorithm that applied is Quasi-Netwon based on the lowest recorded absolute training error. This study come out with the daily future index for 90 days ahead and the index are quite similar with the past historical exchange rate. Hence, it is believed that experimental results demonstrate that Artificial Neural Network based model can closely predict the future exchange rate.
format	Student Project
author	Amran, Ikhwan Muzammil
author_facet	Amran, Ikhwan Muzammil
author_sort	Amran, Ikhwan Muzammil
title	Forecasting Malaysian exchange rate artificial neural network : 2010-2018 / Ikhwan Muzammil Amran
title_short	Forecasting Malaysian exchange rate artificial neural network : 2010-2018 / Ikhwan Muzammil Amran
title_full	Forecasting Malaysian exchange rate artificial neural network : 2010-2018 / Ikhwan Muzammil Amran
title_fullStr	Forecasting Malaysian exchange rate artificial neural network : 2010-2018 / Ikhwan Muzammil Amran
title_full_unstemmed	Forecasting Malaysian exchange rate artificial neural network : 2010-2018 / Ikhwan Muzammil Amran
title_sort	forecasting malaysian exchange rate artificial neural network : 2010-2018 / ikhwan muzammil amran
publisher	Faculty of Computer and Mathematical Sciences
publishDate	2018
url	http://ir.uitm.edu.my/id/eprint/26562/ http://ir.uitm.edu.my/id/eprint/26562/1/Pbb_IKHWAN%20MUZAMMIL%20AMRAN%20CS%20R%2019_5.pdf
first_indexed	2023-09-18T23:17:02Z
last_indexed	2023-09-18T23:17:02Z
_version_	1777419181528973312

Forecasting Malaysian exchange rate artificial neural network : 2010-2018 / Ikhwan Muzammil Amran

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