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An intelligent forex monitoring system

Abraham, Ajith and Chowdhury, Morshed 2001, An intelligent forex monitoring system, in 2001 International Conferences on Info-Tech and Info-Net : proceedings : ICCII 2001-Beijing, October 29-November 1, 2001, Beijing, China, IEEE Xplore, Piscataway, N.J., pp. 523-528.

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Title An intelligent forex monitoring system
Author(s) Abraham, Ajith
Chowdhury, Morshed
Conference name International Conferences on Info-Tech and Info-Net (2001 : Beijing, China)
Conference location Beijing, China
Conference dates 29 October - 1 November 2001
Title of proceedings 2001 International Conferences on Info-Tech and Info-Net : proceedings : ICCII 2001-Beijing, October 29-November 1, 2001, Beijing, China
Editor(s) Zhong, Y.X.
Zhongzhi, S.
Hui, L.
Publication date 2001
Conference series International Conferences on Info-Tech and Info-Net
Start page 523
End page 528
Total pages 6
Publisher IEEE Xplore
Place of publication Piscataway, N.J.
Keyword(s) forex prediction
neurocomputing
neuro-fuzzy computing
scaled conjugate gradient
Summary The need for intelligent monitoring systems has become a necessity to keep track of the complex forex market. The vast currency market is a foreign concept to the average individual. However, once it is broken down into simple terms, the average individual can begin to understand the foreign exchange market and use it as a financial instrument for future investing. We attempt to compare the performance of a Takagi-Sugeno, type neuro-fuzzy system and a feedforward neural network trained using the scaled conjugate gradient algorithm to predict the average monthly forex rates. We considered the exchange values of Australian dollar with respect to US dollar, Singapore dollar, New Zealand dollar, Japanese yen and United Kingdom pounds. The connectionist models were trained using 70% of the data and remaining was used for testing and validation purposes. It is observed that the proposed connectionist models were able to predict the average forex rates one month ahead accurately. Experiment results also reveal that the neuro-fuzzy technique performed better than the neural network
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
ISBN 0780370104
9780780370104
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2001, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30009520

Document type: Conference Paper
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.