Multivariate Hysteretic Autoregressive Models

Chapter 40 : Multivariate autoregressive models

Multivariate Autoregressive Mixture Models for Music Auto-Tagging

We propose the multivariate autoregressive model for content based music auto-tagging. At the song level our approach leverages the multivariate autoregressive mixture (ARM) model, a generative time-series model for audio, which assumes each feature vector in an audio fragment is a linear function of previous feature vectors. To tackle tagmodel estimation, we propose an efficient hierarchical E...

Estimation for Partially Nonstationary Multivariate Autoregressive Models with Conditional Heteroskedasticity

where $‘s inre r*onst,ant matricaes; detI{@(z)} = 11 @,x . w * $JP[ = 0 has ci 5 771, urrit roots and ‘I’ = 711 d roots omside the urrit, circle: tPt = ((1 it, 7 c+> is a sequcnce of independent1 and idcntically distlributled (i.i.tl) matrices with mean zero and nonnegativc covarianc~e IC[ /le+&) ~f’(&)] = 0; pit is an i.i.d ramlom vector witIh mean zero and positive covariance E ( etef j = CA ...

Proper multivariate conditional autoregressive models for spatial data analysis.

In the past decade conditional autoregressive modelling specifications have found considerable application for the analysis of spatial data. Nearly all of this work is done in the univariate case and employs an improper specification. Our contribution here is to move to multivariate conditional autoregressive models and to provide rich, flexible classes which yield proper distributions. Our app...

Tutorial on multivariate autoregressive modelling.

In the present paper, the theoretical background of multivariate autoregressive modelling (MAR) is explained. The motivation for MAR modelling is the need to study the linear relationships between signals. In biomedical engineering, MAR modelling is used especially in the analysis of cardiovascular dynamics and electroencephalographic signals, because it allows determination of physiologically ...