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Forex Forecasting: A Comparative Study of LLWNN and NeuroFuzzy Hybrid Model

by Puspanjali Mohapatra, Munnangi Anirudh, Tapas Kumar Patra
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 66 - Number 18
Year of Publication: 2013
Authors: Puspanjali Mohapatra, Munnangi Anirudh, Tapas Kumar Patra
10.5120/11188-6451

Puspanjali Mohapatra, Munnangi Anirudh, Tapas Kumar Patra . Forex Forecasting: A Comparative Study of LLWNN and NeuroFuzzy Hybrid Model. International Journal of Computer Applications. 66, 18 ( March 2013), 46-53. DOI=10.5120/11188-6451

@article{ 10.5120/11188-6451,
author = { Puspanjali Mohapatra, Munnangi Anirudh, Tapas Kumar Patra },
title = { Forex Forecasting: A Comparative Study of LLWNN and NeuroFuzzy Hybrid Model },
journal = { International Journal of Computer Applications },
issue_date = { March 2013 },
volume = { 66 },
number = { 18 },
month = { March },
year = { 2013 },
issn = { 0975-8887 },
pages = { 46-53 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume66/number18/11188-6451/ },
doi = { 10.5120/11188-6451 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:22:49.336697+05:30
%A Puspanjali Mohapatra
%A Munnangi Anirudh
%A Tapas Kumar Patra
%T Forex Forecasting: A Comparative Study of LLWNN and NeuroFuzzy Hybrid Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 66
%N 18
%P 46-53
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper shows how the performance of the basic Local Linear Wavelet Neural Network model (LLWNN) can be improved with hybridizing it with fuzzy model. The new improved LLWNN based Neurofuzzy hybrid model is used to predict two currency exchange rates i. e. the U. S. Dollar to the Indian Rupee and the U. S. Dollar to the Japanese Yen. The forecasting of foreign exchange rates is done on different time horizons for 1 day, 1 week and 1 month ahead. The LLWNN and Neurofuzzy hybrid models are trained with the backpropagation training algorithm. The two performance measurers i. e. the Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE) show the superiority of the Neurofuzzy hybrid model over the LLWNN model.

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Index Terms

Computer Science
Information Sciences

Keywords

Forex Foreign Exchange Market. LLWNN LLWNN based Neurofuzzy hybrid model Back Propagation training algorithm

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