Dynamic ridge polynomial neural network: forecasting the univariate non-stationary and stationary trading signals

Ghazali, Rozaida and Hussain, Abir Jaafar and Liatsis, Panos (2011) Dynamic ridge polynomial neural network: forecasting the univariate non-stationary and stationary trading signals. Experts Systems and Applications, 38 (4). pp. 3765-3776.

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Abstract

This paper considers the prediction of noisy time series data, specifically, the prediction of financial signals. A novel Dynamic Ridge Polynomial Neural Network (DRPNN) for financial time series prediction is presented which combines the properties of both higher order and recurrent neural network. In an attempt to overcome the stability and convergence problems in the proposed DRPNN, the stability convergence of DRPNN is derived to ensure that the network posses a unique equilibrium state. In order to provide a more accurate comparative evaluation in terms of profit earning, empirical testing used in this work encompass not only on the more traditional criteria of NMSE, which concerned at how good the forecast fit their target, but also on financial metrics where the objectives is to use the network predictions to generate profit.

Item Type:Article
Subjects:Q Science > QA Mathematics > QA295 Series
Divisions:Faculty of Computer Science and Information Technology > Department of Software Engineering
ID Code:3037
Deposited By:Normajihan Abd. Rahman
Deposited On:21 Feb 2013 12:02
Last Modified:22 Jan 2015 08:35

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