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They also build multivariate autoregressive models for a smaller number of links.
Granger causality has been increasingly used to identify causal influence between time series based on multivariate autoregressive models.
The VAR processes are a generalization of multivariate autoregressive models (AR), where each variable is regressed on a set of others with several lags (Hamilton 1994; Enders 2009).
This paper describes a family of enhanced multivariate autoregressive models that can be used in SHM-oriented identification procedures and the implementation of a new advanced SHM system in the tower of the Engineering School of Bologna University.
In this paper a hybrid of statistical (a Markov chain-gamma model for stochastic generation of rainfall and two different multivariate autoregressive models for stochastic generation of air temperature and influent time series in dry conditions) and conceptual modeling techniques is proposed for synthetic generation of influent time series.
In this work, we investigate the feasibility to estimating causal interactions between brain regions based on multivariate autoregressive models (MAR models) fitted to magnetoencephalographic (MEG) sensor measurements.
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Causal coherence[40 42] or partial directed coherence[43, 44], based on multivariate autoregressive modeling may be used for this purpose.
Firstly, the Source Information Flow Toolbox (SIFT) [27] in the EEGLAB was used to identify the optimal multivariate autoregressive model.
A multivariate autoregressive model is presented, and its parameters are estimated by least squares via the implementation of QR factorization.
multiple window time frequency analysis (MW-TFA) [9], frequency-dependant correlation coefficient [10], time varying causal coherence function (TVCCF) based on the multivariate autoregressive model [11].
In order to obtain these conditional probabilities, a multivariate autoregressive model with lag one [AR(1)] is chosen for extending multiple duration rainfall series.
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