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ICASSP
2011
IEEE
12 years 8 months ago
Sparse graphical modeling of piecewise-stationary time series
Graphical models are useful for capturing interdependencies of statistical variables in various fields. Estimating parameters describing sparse graphical models of stationary mul...
Daniele Angelosante, Georgios B. Giannakis
FLAIRS
2009
13 years 2 months ago
Multiagent Bayesian Forecasting of Time Series with Graphical Models
Time series are found widely in engineering and science. We study multiagent forecasting in time series, drawing from literature on time series, graphical models, and multiagent s...
Yang Xiang, James Smith, Jeff Kroes
JMLR
2010
194views more  JMLR 2010»
12 years 11 months ago
Graphical Gaussian modelling of multivariate time series with latent variables
In time series analysis, inference about causeeffect relationships among multiple times series is commonly based on the concept of Granger causality, which exploits temporal struc...
Michael Eichler
NC
2006
132views Neural Networks» more  NC 2006»
13 years 4 months ago
Learning short multivariate time series models through evolutionary and sparse matrix computation
Multivariate Time Series (MTS) data are widely available in different fields including medicine, finance, bioinformatics, science and engineering. Modelling MTS data accurately is...
Stephen Swift, Joost N. Kok, Xiaohui Liu
JMLR
2010
158views more  JMLR 2010»
12 years 11 months ago
Topology Selection in Graphical Models of Autoregressive Processes
An algorithm is presented for topology selection in graphical models of autoregressive Gaussian time series. The graph topology of the model represents the sparsity pattern of the...
Jitkomut Songsiri, Lieven Vandenberghe