Parsimonious Var Models For Air Pollution Dynamic Analysis
Abstract
We discuss a framework to obtain temporal predictions for an evolving spatial field regularly sampled in time at arbitrary spatial locations. Difficulties caused by large data sets and the modelling of complicated spatio-temporal interactions limit the effectiveness of traditional space-time statistical models. In this study, we propose the use of a flexible approach to deal with large and small time-scale variability of the observed data. The temporal model is applied with respect to both the observed data domain and the common component domain, to achieve a dimensionality reduction.
DOI: https://doi.org/10.3844/jmssp.2005.267.276
Copyright: © 2005 Fontanella lara and Granturco mariagrazia. This is an open access article distributed under the terms of the
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Keywords
- VAR models
- State-Space
- Karhunen- Loève Transform
- Trend Surface