An R package for identification of outliers in environmental time series data. (September 2022)

• Record Type: Journal Article
• Authors: Čampulová, Martina; Čampula, Roman; Holešovský, Jan
• Is Part Of: Environmental modelling & software. Volume 155(2022)
• Publication Date: 2022-09
• Subjects: Outlier -- Data validation -- Kernel regression -- Environmental data -- R package; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105435
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Environmental data often include outliers that may significantly affect further modelling and data analysis. Although a number of outlier detection methods have been proposed, their use is usually complicated by the assumption of the distribution or model of the analyzed data. However, environmental variables are quite often influenced by many different factors and their distribution is difficult to estimate. The envoutliers package has been developed to provide users with a choice of recently presented, semi-parametric outlier detection methods that do not impose requirements on the distribution of the original data. This paper briefly describes the methodology as well as its implementation in the package. The application is illustrated on real data examples. Highlights: R package with lately published outlier detection methods for time series. Various methods without requirements on the distribution or stationarity. Further generalization of recent approaches proposed and also implemented. Automated identification of outliers in extensive data. Illustration on real data of O3 concentrations.