The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Artificial Higher Order Pipeline Recurrent Neural Networks for Financial Time Series Prediction
|
|
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
Purchase
|
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.
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.
|
|
|