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» Time Series Prediction by Perturbed Fuzzy Model
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INFOCOM
2009
IEEE
15 years 4 months ago
Measuring Complexity and Predictability in Networks with Multiscale Entropy Analysis
—We propose to use multiscale entropy analysis in characterisation of network traffic and spectrum usage. We show that with such analysis one can quantify complexity and predict...
Janne Riihijärvi, Matthias Wellens, Petri M&a...
ITIIS
2010
138views more  ITIIS 2010»
14 years 4 months ago
Identification of Fuzzy Inference System Based on Information Granulation
In this study, we propose a space search algorithm (SSA) and then introduce a hybrid optimization of fuzzy inference systems based on SSA and information granulation (IG). In comp...
Wei Huang, Lixin Ding, Sung-Kwun Oh, Chang-Won Jeo...
ESANN
2003
14 years 10 months ago
Autonomous learning algorithm for fully connected recurrent networks
In this paper fully connected RTRL neural networks are studied. In order to learn dynamical behaviours of linear-processes or to predict time series, an autonomous learning algori...
Edouard Leclercq, Fabrice Druaux, Dimitri Lefebvre
CSDA
2007
202views more  CSDA 2007»
14 years 9 months ago
Bayesian estimation of the Gaussian mixture GARCH model
In this paper, we perform Bayesian inference and prediction for a GARCH model where the innovations are assumed to follow a mixture of two Gaussian distributions. This GARCH model...
María Concepción Ausín, Pedro...
BMCBI
2011
14 years 1 months ago
A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression
Background: The analysis of gene expression from time series underpins many biological studies. Two basic forms of analysis recur for data of this type: removing inactive (quiet) ...
Alfredo A. Kalaitzis, Neil D. Lawrence