Paper Info
Title
The copula echo state network
Abstract
Echo state networks (ESNs) constitute a novel approach to recurrent neural network (RNN) training, with an RNN (the reservoir) being generated randomly, and only a readout being trained using a simple, computationally efficient algorithm. ESNs have greatly facilitated the practical application of RNNs, outperforming classical approaches on a number of benchmark tasks. This paper studies the formulation of a class of copula-based semiparametric models for sequential data modeling, characterized by nonparametric marginal distributions modeled by postulating suitable echo state networks, and parametric copula functions that help capture all the scale-free temporal dependence of the modeled processes. We provide a simple algorithm for the data-driven estimation of the marginal distribution and the copula parameters of our model under the maximum-likelihood framework. We exhibit the merits of our approach by considering a number of applications; as we show, our method offers a significant enhancement in the dynamical data modeling capabilities of ESNs, without significant compromises in the algorithm's computational efficiency.
Year
DOI
Venue
2012
10.1016/j.patcog.2011.06.022
Pattern Recognition
Keywords
Field
DocType
novel approach,marginal distribution,echo state network,dynamical data,computationally efficient algorithm,copula parameter,nonparametric marginal distribution,copula echo state network,parametric copula function,classical approach,simple algorithm,copula,maximum likelihood
Data modeling,Pattern recognition,Copula (linguistics),Recurrent neural network,Nonparametric statistics,Parametric statistics,Artificial intelligence,Echo state network,SIMPLE algorithm,Machine learning,Mathematics,Marginal distribution
Journal
Volume
Issue
ISSN
45
1
0031-3203
Citations 
PageRank 
References 
3
0.39
22
Authors
2
Name
Order
Citations
PageRank
Sotirios P. Chatzis125024.25
Yiannis Demiris293886.45