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Hybrid Neural Network and Decision Tree for for Exchange Rates Forecasting

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

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Abstract

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

Item Type: Conference or Workshop Item (Speech)
Uncontrolled Keywords: Hybrid system; Artificial neural network; Decision tree; Exchange rates; Forecasting model
Subjects: Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Ms. Ratna Wilis Haryati Mustapa
Date Deposited: 21 Mar 2013 07:41
Last Modified: 21 May 2018 01:53
URI: http://umpir.ump.edu.my/id/eprint/3518
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