Prediction of spatial functional random processes: comparing functional and spatio-temporal kriging approaches

Abstract

We present and compare functional and spatio-temporal (Sp.T.) kriging approaches to predict spatial functional randomprocesses (which can also be viewed as Sp.T. random processes). Comparisons with respect to computational time andprediction performance via functional cross-validation is evaluated, mainly through a simulation study but also on a realdata set. We restrict comparisons to Sp.T. kriging versus ordinary kriging for functional data (OKFD), since the moreflexible functional kriging approaches pointwise functional kriging (PWFK) and the functional kriging total model coincidewith OKFD in several situations. Here we formulate conditions under which we show that OKFD and PWFK coincide.From the simulation study, it is concluded that the prediction performance of the two kriging approaches in general israther equal for stationary Sp.T. processes. However, functional kriging tends to perform better for small sample sizes,while Sp.T. kriging works better for large sizes. For non-stationary Sp.T. processes, with a common deterministic timetrend and/or time varying variances and dependence structure, OKFD performs better than Sp.T. kriging irrespective of thesample size. For all simulated cases, the computational time for OKFD was considerably lower compared to those for theSp.T. kriging methods