High-Resolution Spatiotemporal Forecasting with Missing Observations Including an Application to Daily Particulate Matter 2.5 Concentrations in Jakarta Province, Indonesia

High-Resolution Spatiotemporal Forecasting with Missing Observations Including an Application to Daily Particulate Matter 2.5 Concentrations in Jakarta Province, Indonesia

Accurate forecasting of high-resolution particulate matter 2.5 (PM<sub>2.5</sub>) levels is essential for the development of public health policy. However, datasets used for this purpose often contain missing observations. This study presents a two-stage approach to handle this problem....

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Bibliographic Details
Main Authors: I Gede Nyoman Mindra Jaya, Henk Folmer
Format: Article
Language:English
Published: MDPI AG 2024-09-01
Series:Mathematics
Subjects:
multivariate spatial time series model
Gaussian Markov random field (GMRF)
high-resolution forecasting
Bayesian statistics
integrated nested Laplace approximation (INLA)
PM<sub>2.5</sub>
Online Access:https://www.mdpi.com/2227-7390/12/18/2899
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