Polynomial pipelined neural network and its application to financial time series prediction

Authors:
Abir Jaafar Hussain;Adam Knowles;Paulo Lisboa;Wael El-Deredy;Dhiya Al-Jumeily
Affiliations:
Liverpool John Moores University, Liverpool, UK;Liverpool John Moores University, Liverpool, UK;Liverpool John Moores University, Liverpool, UK;University of Manchester, Manchester;Liverpool John Moores University, Liverpool, UK
Venue:
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Year:
2006

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Abstract

A novel type of higher order pipelined neural network, the polynomial pipelined neural network, is presented. The network is constructed from a number of higher order neural networks concatenated with each other to predict highly nonlinear and nonstationary signals based on the engineering concept of divide and conquer. It is evaluated in financial time series application to predict the exchange rate between the US Dollar and 3 other currencies. The network demonstrates more accurate forecasting and an improvement in the signal to noise ratio over a number of benchmarked neural network.