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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
computer science
A wavelet-based multiscale vector-ANN model to predict comovement of econophysical systems
Expert Systems with Applications, Volume 41, No. 13, Year 2014
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Description
The paper proposes a parsimonious nonlinear framework for modeling bivariate stochastic processes. The method is a vector autoregressive-like approach equipped with a wavelet-based feedforward neural network, allowing practitioners dealing with extremely random two-dimensional information to make predictions and plan their future more and more precisely. Artificial Neural Networks (ANN) are recognized as powerful computing devices and universal approximators that proved valuable for a wide range of univariate time series problems. We expand their coverage to handle nonlinear bivariate data. Wavelet techniques are used to strengthen the procedure, since they allow to break up processes information into a finite number of sub-signals, and subsequently extract microscopic patterns in both time and frequency fields. The proposed model can be very valuable especially when modeling nonlinear econophysical systems with high extent of volatility. © 2014 Elsevier Ltd. All rights reserved.
Authors & Co-Authors
Saâdaoui, Foued
Tunisia, Sousse
Higher Institute of Applied Sciences and Technology of Sousse
Rabbouch, Hana
Tunisia, Manouba
Ecole Nationale Des Sciences de L'informatique
Statistics
Citations: 34
Authors: 2
Affiliations: 2
Identifiers
Doi:
10.1016/j.eswa.2014.03.030
ISSN:
09574174
Research Areas
Health System And Policy