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ČAMPULOVÁ, M. ČAMPULA, R. HOLEŠOVSKÝ, J.
Original Title
An R package for identification of outliers in environmental time series data
Type
journal article in Web of Science
Language
English
Original 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.
Keywords
Outlier; Data validation; Kernel regression; Environmental data; R package
Authors
ČAMPULOVÁ, M.; ČAMPULA, R.; HOLEŠOVSKÝ, J.
Released
17. 8. 2022
Publisher
Elsevier
Location
Amsterdam
ISBN
1364-8152
Periodical
ENVIRONMENTAL MODELLING & SOFTWARE
Year of study
155
Number
1
State
United Kingdom of Great Britain and Northern Ireland
Pages from
Pages to
18
Pages count
URL
https://pdf.sciencedirectassets.com/271872/1-s2.0-S1364815222X00066/1-s2.0-S1364815222001414/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEMv%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJIMEYCIQCABrXH%2BDuWf%2B%2B0OCEjAGscLGJgzmHXUKShx53gqrtk%2FAIhAPdrGiB%2F2IKift3R96EFSLR%2BQqZs3yZquo0dGlmddqPYKtsECNT%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEQBBoMMDU5MDAzNTQ2ODY1IgzgXROrZoM9BjdM%2FDwqrwTm%2FTbuH1A9xg4Q7K%2FY7amewXMUHfD%2BGVQNn8EmXUwUml4hSRfYrrf94bL84IpNiNPOs0RmANltVk4hl5%2BlO1yOXvNf%2BJwVS7ESdB4eusSvrD%2ByhGet4CG
BibTex
@article{BUT178243, author="Martina {Čampulová} and Roman {Čampula} and Jan {Holešovský}", title="An R package for identification of outliers in environmental time series data", journal="ENVIRONMENTAL MODELLING & SOFTWARE", year="2022", volume="155", number="1", pages="1--18", doi="10.1016/j.envsoft.2022.105435", issn="1364-8152", url="https://pdf.sciencedirectassets.com/271872/1-s2.0-S1364815222X00066/1-s2.0-S1364815222001414/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEMv%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJIMEYCIQCABrXH%2BDuWf%2B%2B0OCEjAGscLGJgzmHXUKShx53gqrtk%2FAIhAPdrGiB%2F2IKift3R96EFSLR%2BQqZs3yZquo0dGlmddqPYKtsECNT%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEQBBoMMDU5MDAzNTQ2ODY1IgzgXROrZoM9BjdM%2FDwqrwTm%2FTbuH1A9xg4Q7K%2FY7amewXMUHfD%2BGVQNn8EmXUwUml4hSRfYrrf94bL84IpNiNPOs0RmANltVk4hl5%2BlO1yOXvNf%2BJwVS7ESdB4eusSvrD%2ByhGet4CG" }