IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

A Novel Recurrent Polynomial Neural Network for Financial Time Series Prediction

A Novel Recurrent Polynomial Neural Network for Financial Time Series Prediction
View Sample PDF
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

Purchase

View A Novel Recurrent Polynomial Neural Network for Financial Time Series Prediction on the publisher's website for pricing and purchasing information.

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.

Related Content

Arunaben Prahladbhai Gurjar, Shitalben Bhagubhai Patel. © 2022. 30 pages.
Meghna Babubhai Patel, Jagruti N. Patel, Upasana M. Bhilota. © 2022. 10 pages.
Vo Ngoc Phu, Vo Thi Ngoc Tran. © 2022. 27 pages.
Steven Walczak. © 2022. 17 pages.
Priyanka P. Patel, Amit R. Thakkar. © 2022. 26 pages.
Vo Ngoc Phu, Vo Thi Ngoc Tran. © 2022. 34 pages.
Sarat Chandra Nayak, Subhranginee Das, Bijan Bihari Misra. © 2022. 20 pages.
Body Bottom