Graphical interaction models for multivariate time series1

By Rainer Dahlhaus. 2000

373 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/0e8541198d065672cb81d9cb368066af12164f0b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Signal Processing

Citations in the corpus

Graphical models for marked point processes based on local independence (2007, background): “where A⊥ B|C means that ‘A is conditionally independent of B given C’ (see Dawid (1979)).”
Sparse Vector Autoregressive Modeling (2012, methodology): “The removal of the linear effect of {Yt,−ij } from each of {Yt,i} and {Yt,j } can be achieved by using results of linear filters, for example, see Brillinger (1981) and Dahlhaus (2000).”
Testing for directed influences among neural signals using partial directed coherence. (2006, neutral): “In particular, signiﬁcance levels for testing for nonzero partial directed coherences at ﬁxed frequencies are not available and are (Baccala and Sameshima, 2001 covariance matrix of stationarity of usually determined by simulations ; Schnider et al., 1989).”
Elliptical graphical modelling (2011, background): “in Dahlhaus (2000). More sophisticated model search procedures, such as backward elimination, showed strictly better results (in terms of mean edge difference), but of comparable magnitude and lead to the same conclusions as far as the comparison of the estimators is concerned.”
NETS: Network estimation for time series (2019, neutral): “Several network representations can be associated with a var system (Dahlhaus, 2000; Eichler, 2007; Diebold and Yılmaz, 2014).”
Testing nonparametric and semiparametric hypotheses in vector stationary processes (2008, background): “More general hypotheses about the interrelation structure in multivariate time series can be formulated in terms of partial coherences; such hypotheses arise, for example, in graphical interaction modelling based on partial correlation graphs [14,13].”
Sparse Multi-Output Gaussian Processes for Medical Time Series Prediction (2017, methodology): “Time series graphical models (TGMs) (Dahlhaus, 2000; Tank et al., 2015) have also been studied and applied for analyzing multivariate medical time series of ICU patients (Gather et al., 2002).”
Analysis of air quality time series of Hong Kong with graphical modeling (2016, methodology): “Brillinger (1996), Dahlhaus et al. (1997), Dahlhaus (2000), and Eichler (2012) attempt to extend the graphical methodology to time series data. More recently, Jung et al. (2015), Khare et al. (2015), Qiu et al. (2015), and Wolstenholme and Walden (2015) proposed other methods in determining a graphi”