Graphical interaction models for multivariate time series 1

Fitting Graphical Interaction Models to Multivariate Time Series

Graphical interaction models have become an important tool for analysing multivariate time series. In these models, the interrelationships among the components of a time series are described by undirected graphs in which the vertices depict the components while the edges indictate possible dependencies between the components. Current methods for the identification of the graphical structure are...

Graphical models for multivariate time series from intensive care monitoring.

Nowadays physicians are confronted with high-dimensional data generated by clinical information systems. The proper extraction and interpretation of the information contained in such massive data sets, which are often observed with high sampling frequencies, can hardly be done by experience only. This yields new perspectives of data recording and also sets a new challenge for statistical method...

Decomposability and selection of graphical models for multivariate time series

Graphical modelling for multivariate time series

Graphical models for multivariate time series is a concept extended by Dahlhaus (2000) from a random vector to a time series. We propose a test statistic to identify a graphical model for multivariate time series with the Kullback-Leibler distance between two spectral density matrices characterized by graphical models. Asymptotic null distribution is derived to be normal with the mean and varia...

Estimating Dynamic Graphical Models from Multivariate Time-series Data

We consider the problem of estimating dynamic graphical models that describe the time-evolving conditional dependency structure between a set of data-streams. The bulk of work in such graphical structure learning problems has focused in the stationary i.i.d setting. However, when one introduces dynamics to such models we are forced to make additional assumptions about how the estimated distribu...