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 paper, 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.
Parsimonious VAR Models for Air Pollution Dynamic Analysis
FONTANELLA, Lara;
2005-01-01
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 paper, 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.File in questo prodotto:
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