A spatial time series framework is used for stochastic modelling of daily average Sulphur Dioxide (SO2) levels in the Milan district. Within a spatiotemporal Kalman filter algorithm, stochastic conditional simulation is performed to obtain spatial and temporal predictions of the observed process. Unlike other recent space-time Kalman filters, the inclusion of a point source trend model also allows the development of a spatio-temporal state-space model that achieves dimension reduction in the analysis of large data set.
Conditional Simulation in Dynamic Linear Models for Spatial and Temporal Predictions of Diffusive Phenomena
DI BATTISTA, Tonio;FONTANELLA, Lara;IPPOLITI, Luigi
2003-01-01
Abstract
A spatial time series framework is used for stochastic modelling of daily average Sulphur Dioxide (SO2) levels in the Milan district. Within a spatiotemporal Kalman filter algorithm, stochastic conditional simulation is performed to obtain spatial and temporal predictions of the observed process. Unlike other recent space-time Kalman filters, the inclusion of a point source trend model also allows the development of a spatio-temporal state-space model that achieves dimension reduction in the analysis of large data set.File in questo prodotto:
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