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Artificial Higher Order Pipeline Recurrent Neural Networks for Financial Time Series Prediction

Artificial Higher Order Pipeline Recurrent Neural Networks for Financial Time Series Prediction
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Author(s): Panos Liatsis (City University, London, UK), Abir Hussain (John Moores University, UK) and Efstathios Milonidis (City University, London, UK)
Copyright: 2009
Pages: 26
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.ch008

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Abstract

The research described in this chapter is concerned with the development of a novel artificial higher order neural networks architecture called the second-order pipeline recurrent neural network. The proposed artificial neural network consists of a linear and a nonlinear section, extracting relevant features from the input signal. The structuring unit of the proposed neural network is the second-order recurrent neural network. The architecture consists of a series of second-order recurrent neural networks, which are concatenated with each other. Simulation results in one-step ahead predictions of the foreign currency exchange rates demonstrate the superior performance of the proposed pipeline architecture as compared to other feed-forward and recurrent structures.

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