Search results for " EOF"
showing 3 items of 3 documents
Filling in long gap sequences by performing jointly EOF and FDA
2012
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 …
Functional Principal Component Analysis for the explorative analysis of multisite-multivariate air pollution time series with long gaps
2013
The knowledge of the urban air quality represents the first step to face air pollution issues. For the last decades many cities can rely on a network of monitoring stations recording concentration values for the main pollutants. This paper focuses on functional principal component analysis (FPCA) to investigate multiple pollutant datasets measured over time at multiple sites within a given urban area. Our purpose is to extend what has been proposed in the literature to data that are multisite and multivariate at the same time. The approach results to be effective to highlight some relevant statistical features of the time series, giving the opportunity to identify significant pollutants and…
EOFs for gap filling in multivariate air quality data: a FDA approach
2010
Missing values are a common concern in spatiotemporal data sets. During recent years a great number of methods have been developed for gap filling. One of the emerging approaches is based on the Empirical Orthogonal Function (EOF) methodology, applied mainly on raw and univariate data sets presenting irregular missing patterns. In this paper EOF is carried out on a multivariate space-time data set, related to concentrations of pollutants recorded at different sites, after denoising raw data by FDA approach. Some performance indicators are computed on simulated incomplete data sets with also long gaps in order to show that the EOF reconstruction appears to be an improved procedure especially…