Space-Time Covariance Structures and Models

In recent years, interest has grown in modeling spatio-temporal data generated from monitoring networks, satellite imaging, and climate models. Under Gaussianity, the covariance function is core to modeling, inference, prediction. this article, we review various space-time structures which simplified assumptions, such as separability full symmetry, are made facilitate computation, associated tests intended validate these structures. We also developments on constructing models, can be separable or nonseparable, fully symmetric asymmetric, stationary nonstationary, univariate multivariate, Euclidean spaces sphere. visualize some of models with visuanimations. Finally, discuss inference for fitting describe a case study based new wind-speed set.