Filling in long gap sequences by performing jointly EOF and FDA

Abstract

In this paper the EOF methodology is performed jointly with the FDA approach on a spatiotemporal multivariate data set with the aim to fill in missing values as accurately as possible when long gap sequences occur. Simulated data sets, containing ”artificial” gaps, are considered in order to test the performance of two proposed procedures; in the first one, observed data are reconstructed by EOF and then converted into functional ones; in the second one, observed data are transformed into functional ones and then EOF reconstruction is applied. By comparing some performance indicators computed for the two procedures, it is shown that a pre-processing of data by FDA, followed by the EOF, may result in a better reconstruction.