Title: Dynamic Ridge Polynomial Neural Networks for multi-step financial time-series prediction

Authors: Abir Jaafar Hussain, Rozaida Ghazali, Dhiya Al-Jumeily

Addresses: School of Computing and Mathematical Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK. ' School of Computing and Mathematical Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK. ' School of Computing and Mathematical Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK

Abstract: Concerned with the slow learning problems of multilayer perceptrons, which utilise computationally intensive training algorithms like the backpropagation learning algorithm that can get trapped in local minima, this paper presents a novel type of higher-order recurrent neural networks, which is called the Dynamic Ridge Polynomial Neural Networks (DRPNN). The aim of the proposed network is to improve the performance of the Ridge Polynomial Neural Network by accommodating recurrent links structure. The network is tested for the prediction of non-linear and non-stationary financial signals. Three exchange rate time-series, which are the exchange rate time series between the British Pound and the Euro, the US dollar and the Euro as well as the exchange rate between the Japanese Yen and the Euro, are used in the simulation process. Simulation results showed that DRPNN generate higher profit returns with fast convergence when used to predict noisy financial time series.

Keywords: exchange rate time-series; financial time-series prediction; recurrent neural networks; ridge polynomial neural networks; RPNN; recurrent links; British pound sterling; Euro; US dollar; Japanese Yen; simulation.

DOI: 10.1504/IJISTA.2008.018171

International Journal of Intelligent Systems Technologies and Applications, 2008 Vol.5 No.1/2, pp.145 - 165

Available online: 05 May 2008 *

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