BAYESIAN AND MULTIVARIATE AUTOREGRESSIVE

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 ...

Chapter 40 : Multivariate autoregressive models

Bayesian Model Selectionof Autoregressive

This paper poses the problem of model order determination of an autoregressive (AR) pro ess within a Bayesian framework. Several original hierar hi al prior models are proposed that allow for the stability of the model to be enfor ed and a ount for a possible unknown initial state. Obtaining the posterior model order probabilities requires integration of the resulting posterior distribution, an...

Bayesian Mixtures of Autoregressive Models

In this paper we propose a class of time-domain models for analyzing possibly nonstationary time series. This class of models is formed as a mixture of time series models, whose mixing weights are a function of time. We consider specifically mixtures of autoregressive models with a common but unknown lag. To make the methodology work we show that it is necessary to first partition the data into...

Multivariate Short-term Traffic Flow Forecasting using Bayesian Vector Autoregressive Mov-

1 Short-term Traffic Flow Forecasting (STFF), the process of predicting future traffic conditions 2 based on historical and real-time observations is an essential aspect of Intelligent Transportation 3 Systems (ITS). The existing well-known algorithms used for STFF include time-series analysis 4 based techniques, among which the seasonal Autoregressive Moving Average (ARMA) model 5 is one of th...