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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| Format: | Student Project |
| Language: | English |
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Faculty of Computer and Mathematical Sciences
2018
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| Online Access: | https://ir.uitm.edu.my/id/eprint/26562/ |
| _version_ | 1848806547497418752 |
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| author | Amran, Ikhwan Muzammil |
| author_facet | Amran, Ikhwan Muzammil |
| author_sort | Amran, Ikhwan Muzammil |
| building | UiTM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T22:28:40Z |
| format | Student Project |
| id | uitm-26562 |
| institution | Universiti Teknologi MARA |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T22:28:40Z |
| publishDate | 2018 |
| publisher | Faculty of Computer and Mathematical Sciences |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uitm-265622019-11-25T08:53:57Z https://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 https://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. (2018) [Student Project] (Unpublished) |
| spellingShingle | Neural networks (Computer science) Artificial immune systems. Immunocomputers Amran, Ikhwan Muzammil Forecasting Malaysian exchange rate artificial neural network : 2010-2018 / Ikhwan Muzammil Amran |
| title | 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_short | 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 |
| topic | Neural networks (Computer science) Artificial immune systems. Immunocomputers |
| url | https://ir.uitm.edu.my/id/eprint/26562/ |