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

Authors:
Abir Jaafar Hussain;Rozaida Ghazali;Dhiya Al-Jumeily
Affiliations:
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
Venue:
International Journal of Intelligent Systems Technologies and Applications
Year:
2008

Citing 7
Cited 0

Product units: a computationally powerful and biologically plausible extension to backpropagation networks

Neural Computation
Neural Networks for Identification, Prediction, and Control

Neural Networks for Identification, Prediction, and Control
On a higher-order neural network for distortion invariant pattern recognition

Pattern Recognition Letters
Regression neural network for error correction in foreign exchange forecasting and trading

Computers and Operations Research
Neural Networks: A Comprehensive Foundation (3rd Edition)

Neural Networks: A Comprehensive Foundation (3rd Edition)
A learning algorithm for continually running fully recurrent neural networks

Neural Computation
Ridge polynomial networks

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

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.