A Novel Recurrent Polynomial Neural Network for Financial Time Series Prediction

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Author(s): Abir Hussain (John Moores University, UK)and Panos Liatsis (City University, London, UK)
Copyright: 2009
Pages: 22
Source title: Artificial Higher Order Neural Networks for Economics and Business
Source Author(s)/Editor(s): Ming Zhang (Christopher Newport University, USA)
DOI: 10.4018/978-1-59904-897-0.ch009

Keywords: Artificial Intelligence / Computer Science & IT / Engineering Science Reference / Neural Networks

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Abstract

The research described in this chapter is concerned with the development of a novel artificial higherorder neural networks architecture called the recurrent Pi-sigma neural network. The proposed artificial neural network combines the advantages of both higher-order architectures in terms of the multi-linear interactions between inputs, as well as the temporal dynamics of recurrent neural networks, and produces highly accurate one-step ahead predictions of the foreign currency exchange rates, as compared to other feedforward and recurrent structures.

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A Novel Recurrent Polynomial Neural Network for Financial Time Series Prediction

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