Dynamic Ridge Polynomial Neural Networks in Exchange Rates Time Series Forecasting

Authors:
Rozaida Ghazali;Abir Jaafar Hussain;Dhiya Al-Jumeily;Madjid Merabti
Affiliations:
School of Computing & Mathematical Sciences, Liverpool John Moores University, L3 3AF Liverpool, England;School of Computing & Mathematical Sciences, Liverpool John Moores University, L3 3AF Liverpool, England;School of Computing & Mathematical Sciences, Liverpool John Moores University, L3 3AF Liverpool, England;School of Computing & Mathematical Sciences, Liverpool John Moores University, L3 3AF Liverpool, England
Venue:
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Year:
2007

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Abstract

This paper proposed a novel dynamic system which utilizes Ridge Polynomial Neural Networks for the prediction of the exchange rate time series. We performed a set of simulations covering three uni-variate exchange rate signals which are; the JP/EU, JP/UK, and JP/US time series. The forecasting performance of the novel Dynamic Ridge Polynomial Neural Network is compared with the performance of the Multilayer Perceptron and the feedforward Ridge Polynomial Neural Network. The simulation results indicated that the proposed network demonstrated advantages in capturing noisy movement in the exchange rate signals with a higher profit return.