Bayesian mixture of autoregressive models

Authors:
John W. Lau;Mike K. P. So
Affiliations:
School of Statistics and Actuarial Science, University of the Witwatersrand, Private Bag 3, WITS 2050, Johannesburg, South Africa;Department of Information and Systems Management, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
Venue:
Computational Statistics & Data Analysis
Year:
2008

Citing 2
Cited 1

Time Series Analysis: Forecasting and Control

Time Series Analysis: Forecasting and Control
Cluster and Calendar Based Visualization of Time Series Data

INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization

A Monte Carlo Markov chain algorithm for a class of mixture time series models

Statistics and Computing

Quantified Score

Hi-index	0.03

Visualization

Abstract

An infinite mixture of autoregressive models is developed. The unknown parameters in the mixture autoregressive model follow a mixture distribution, which is governed by a Dirichlet process prior. One main feature of our approach is the generalization of a finite mixture model by having the number of components unspecified. A Bayesian sampling scheme based on a weighted Chinese restaurant process is proposed to generate partitions of observations. Using the partitions, Bayesian prediction, while accounting for possible model uncertainty, determining the most probable number of mixture components, clustering of time series and outlier detection in time series, can be done. Numerical results from simulated and real data are presented to illustrate the methodology.