Title :
Bayesian Inferences and Forecasting in Spatial Time Series Models
Author :
Lee, Sung Duck ; Kim, Duck-Ki
Author_Institution :
Dept. of Inf. & Stat., Chungbuk Nat. Univ., Cheongju, South Korea
Abstract :
The spatial time series data can be viewed as a set of time series collected simultaneously at a number of spatial locations with time. For example, The Mumps data have a feature to infect adjacent broader regions in accordance with spatial location and time. Therefore, The spatial time series models have many parameters of space and time. In this paper, We propose the method of Bayesian inferences and prediction in spatial time series models with a Gibbs Sampler in order to overcome convergence problem in numerical methods. Our results are illustrated by using the data set of mumps cases reported from the Korea Center for Disease Control and Prevention monthly over the years 2001-2009, as well as a simulation study.
Keywords :
Bayes methods; convergence of numerical methods; inference mechanisms; time series; Bayesian forecasting; Bayesian inferences; Gibbs sampler; Mumps data; convergence problem; numerical methods; spatial time series models; Bayesian methods; Covariance matrix; Data models; Mathematical model; Numerical models; Predictive models; Time series analysis;
Conference_Titel :
Machine Learning and Applications (ICMLA), 2010 Ninth International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
978-1-4244-9211-4
DOI :
10.1109/ICMLA.2010.170