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

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International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 66 - Number 18
Year of Publication: 2013
Authors:
Puspanjali Mohapatra
Munnangi Anirudh
Tapas Kumar Patra
10.5120/11188-6451

Puspanjali Mohapatra, Munnangi Anirudh and Tapas Kumar Patra. Article: Forex Forecasting: A Comparative Study of LLWNN and NeuroFuzzy Hybrid Model. International Journal of Computer Applications 66(18):46-53, March 2013. Full text available. BibTeX

@article{key:article,
	author = {Puspanjali Mohapatra and Munnangi Anirudh and Tapas Kumar Patra},
	title = {Article: Forex Forecasting: A Comparative Study of LLWNN and NeuroFuzzy Hybrid Model},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {66},
	number = {18},
	pages = {46-53},
	month = {March},
	note = {Full text available}
}

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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