EOFs for gap filling in multivariate air quality data: a FDA approach

Risultato della ricerca: Otherpeer review


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 when long gap sequences occur.
Lingua originaleEnglish
Numero di pagine8
Stato di pubblicazionePublished - 2010


Entra nei temi di ricerca di 'EOFs for gap filling in multivariate air quality data: a FDA approach'. Insieme formano una fingerprint unica.

Cita questo