Data-Driven Spatio-Temporal Modeling Using the Integro-Difference Equation

Author

Dewar, Michael ; Scerri, Kenneth ; Kadirkamanathan, Visakan

Author_Institution

Sch. of Inf., Univ. of Edinburgh, Edinburgh

Volume

57

Issue

1

fYear

2009

Firstpage

83

Lastpage

91

Abstract

A continuous-in-space, discrete-in-time dynamic spatio-temporal model known as the integro-difference equation (IDE) model is presented in the context of data-driven modeling. A novel decomposition of the IDE is derived, leading to state-space representation that does not couple the number of states with the number of observation locations or the number of parameters. Based on this state-space model, an expectation-maximization (EM) algorithm is developed in order to jointly estimate the IDE model´s spatial field and spatial mixing kernel. The resulting modeling framework is demonstrated on a set of examples.

Keywords

expectation-maximisation algorithm; integro-differential equations; modelling; continuous-in-space model; data-driven spatio-temporal modeling; discrete-in-time dynamic spatio-temporal model; expectation-maximization algorithm; integro-difference equation model; modeling framework; spatial mixing kernel; state-space model; state-space representation; Dynamic spatio-temporal modeling; Integro-Difference Equation (IDE); expectation-maximization (EM) algorithm; maximum-likelihood parameter estimation; state-space;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/TSP.2008.2005091

Filename

4623135