Hybrid Neural Network and Decision Tree for for Exchange Rates Forecasting
As the largest financial market in the world, foreign exchange (Forex) is becoming a very profitable market with a daily transaction of more than 3.0 trillion U.S. dollars. Therefore, predicting about it has been a challenge for many years. Artificial Neural Network (ANN) provides better performance...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2012
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/3518/1/6-ICoCSIM.pdf |
| _version_ | 1825821406567333888 |
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| author | Ardiansyah, Soleh Mazlina, Abdul Majid Jasni, Mohamad Zain |
| author_facet | Ardiansyah, Soleh Mazlina, Abdul Majid Jasni, Mohamad Zain |
| author_sort | Ardiansyah, Soleh |
| collection | UMP |
| description | As the largest financial market in the world, foreign exchange (Forex) is becoming a very profitable market with a daily transaction of more than 3.0 trillion U.S. dollars. Therefore, predicting about it has been a challenge for many years. Artificial Neural Network (ANN) provides better performance of forecasting but it tends to get stuck in local minima and there is no optimal way to determine the best classifier on it. Meanwhile, Decision Tree (DT) is able to generate classifier in the form of a tree. This paper proposes a hybrid prediction model by combining both ANN and DTalgorithm to predict exchange rates. The models are constructed by using the better of parameters and architectures based on related work such as filtering mechanism, number of hidden layers, number of hidden neurons, training algorithm, and error measurement, with the assumption that if the hybrid model is constructed by the better parameters and architectures, then the output of the model also produces better result |
| first_indexed | 2024-03-06T11:41:04Z |
| format | Conference or Workshop Item |
| id | UMPir3518 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T11:41:04Z |
| publishDate | 2012 |
| record_format | dspace |
| spelling | UMPir35182018-05-21T01:53:43Z http://umpir.ump.edu.my/id/eprint/3518/ Hybrid Neural Network and Decision Tree for for Exchange Rates Forecasting Ardiansyah, Soleh Mazlina, Abdul Majid Jasni, Mohamad Zain QA76 Computer software As the largest financial market in the world, foreign exchange (Forex) is becoming a very profitable market with a daily transaction of more than 3.0 trillion U.S. dollars. Therefore, predicting about it has been a challenge for many years. Artificial Neural Network (ANN) provides better performance of forecasting but it tends to get stuck in local minima and there is no optimal way to determine the best classifier on it. Meanwhile, Decision Tree (DT) is able to generate classifier in the form of a tree. This paper proposes a hybrid prediction model by combining both ANN and DTalgorithm to predict exchange rates. The models are constructed by using the better of parameters and architectures based on related work such as filtering mechanism, number of hidden layers, number of hidden neurons, training algorithm, and error measurement, with the assumption that if the hybrid model is constructed by the better parameters and architectures, then the output of the model also produces better result 2012-12-03 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/3518/1/6-ICoCSIM.pdf Ardiansyah, Soleh and Mazlina, Abdul Majid and Jasni, Mohamad Zain (2012) Hybrid Neural Network and Decision Tree for for Exchange Rates Forecasting. In: Proceedings of the First International Conference on Computational Science and Information Management (ICoCSIM2012) , 3-5 December 2012 , Toba Lake, North Sumatra, Indonesia. pp. 29-35., 1. ISBN 978-967-0120-60-7 (Published) |
| spellingShingle | QA76 Computer software Ardiansyah, Soleh Mazlina, Abdul Majid Jasni, Mohamad Zain Hybrid Neural Network and Decision Tree for for Exchange Rates Forecasting |
| title | Hybrid Neural Network and Decision Tree for for Exchange Rates Forecasting |
| title_full | Hybrid Neural Network and Decision Tree for for Exchange Rates Forecasting |
| title_fullStr | Hybrid Neural Network and Decision Tree for for Exchange Rates Forecasting |
| title_full_unstemmed | Hybrid Neural Network and Decision Tree for for Exchange Rates Forecasting |
| title_short | Hybrid Neural Network and Decision Tree for for Exchange Rates Forecasting |
| title_sort | hybrid neural network and decision tree for for exchange rates forecasting |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/3518/1/6-ICoCSIM.pdf |
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