Modelling spatial time series by graphical models

Yuan Li; Heung Wong; Qiang Xia; Fengjing Cai

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Modelling spatial time series by graphical models

Yuan Li 1, Heung Wong 2, Qiang Xia 3, Fengjing Cai 4

1. School of Economics and Statistics, Guangzhou University, Guangzhou, Guangdong 510006, China

2. Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China

3. Department of Applied Mathematics, South China Agricultural University, Guangzhou, Guangdong 510642, China

4. School of Mathematics and Information Science, Wenzhou University, Wenzhou, Zhejiang 325035, China

*Correspondence author

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Funding: SRFDP （No.20124410110002）

Opened online:22 September 2014

Accepted by: none

Citation: Yuan Li, Heung Wong, Qiang Xia.Modelling spatial time series by graphical models[OL]. [22 September 2014] http://en.paper.edu.cn/en_releasepaper/content/4610134

We propose the spatial temporal autoregressive models using a graphical approach.Our model extends the STARIMA model in the sense that ours does not require prior knowledgeabout the graph or weight matrices and can be applied to multivariate cases. Compared with VAR models,our models are parsimonious in parameters and the structure of the covariance matrix in the model is largely simplified.Based on the concept of Granger causality, we first define the spatial temporal chain graph for spatial time series.With the chain graph, the spatial temporal autoregressive model is constructed.Model building procedures are established by selection of graphs and Bayesian method.Simulation results and an application to the study of air pollution in the Pearl River Delta of China are reported.

Keywords:Spatial time series, causality, graphical models.

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