Long gaps in multivariate spatio-temporal data: an approach based on functional data analysis

The main aim of this paper is to perform Functional Principal Component Analysis (FPCA) taking into account spatio-temporal correlation structures, in order to fill in missing values in spatio-temporal multivariate data set. A spatial and a spatio-temporal variant of the classical temporal FPCA is considered; in other words, FPCA is carried out after modeling data with respect to more than one dimension: space (long, lat) or space+time. Moreover, multidimensional FPCA is extended to multivariate context (more than one variable). Information on spatial or spatiotemporal structures are efficiently extracted by applying Generalized Additive Models (GAMs). Both simulation studies and some performance indicators are used to validate the proposed procedure, showing that, especially in presence of long gaps, spatio-temporal FPCA provides a better reconstruction than spatial FPCA.