An Improved Methodology for Filling Missing Values in Spatiotemporal Climate Dataset: Application to Tanganyika Lake Dataset

In this paper, an improved methodology for the determination of
missing values in a spatio-temporal database is presented. This methodology
performs denoising projection in order to accurately fill the missing values
in the database. The improved methodology is called EOF Pruning and it
is based on an original linear projection method called Empirical Orthogo-
nal Functions (EOF). The experiments demonstrate the performance of the
improved methodology and present a comparison with the original EOF and
with a widely-used Optimal Interpolation method called Objective Analysis.