Sentence examples for multivariate autoregressive processes from inspiring English sources

Examples include Markov models [15], [16], multivariate autoregressive processes [17], dynamic Bayesian models [12], [18], EWMA with non-heterogeneity spatial smoothing [14], Bayesian information fusion networks [19] and Bayesian hierarchical models with meteorological factors [20], which have been adopted in several studies for influenza surveillance.

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).

An integrated framework consisting of a multivariate autoregressive (AR) model and multi-way principal component analysis (MPCA) is described for the monitoring of the performance of a batch process.

The multivariate autoregressive (MAR) model was combined with the relative power contribution (RPC) in this analysis.

They also build multivariate autoregressive models for a smaller number of links.

Fin and rudder multivariate hybrid control system were designed using multivariate autoregressive model with multi-input (yaw and roll motion) and multi-output (rudder and fin angle), named MAFRCS (Multivariate Autoregressive Fin and Rudder hybrid Control System).

A multivariate autoregressive model is presented, and its parameters are estimated by least squares via the implementation of QR factorization.

The framework presented in this study combines the ISp method with multivariate autoregressive (MAR) modeling of stochastic excitations.

The predictions obtained to this point are referred to as the MARFS (Multivariate AutoRegressive Forecast of Seismicity) predictions.

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.