Outlier identification based on local extreme quantile estimation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F16%3APU119354" target="_blank" >RIV/00216305:26110/16:PU119354 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Outlier identification based on local extreme quantile estimation

Popis výsledku v původním jazyce

An extensive time series observations serve for an input in wide range of technical, economical and environmental application areas. However, the verification of validity of such data is necessary condition for any further analysis. Correctness of the data can be proven with respect to various criteria, mainly the attention is focused on detecting possible outliers in the series. Among others, these comprise observations corrupted by failure of any measuring instrument or influence of other than the quantity of interest. In this contribution we present an advanced technique for time series outlier detection based on extreme value analysis. Extreme value theory is being successfully applied in many branches, and hence provides an adequate framework for detection of rare events such as outliers. The suitability of the method proposed is also discussed with respect to eventual automation of the whole procedure. The method was applied for validation of hourly air pollution data obtained in Brno, Czech Republic. The measurements were provided by automated instruments at locations with high traffic and industrial load. The proposed method might simplify the procedure of such extensive data verification.

Název v anglickém jazyce

Outlier identification based on local extreme quantile estimation

Popis výsledku anglicky

An extensive time series observations serve for an input in wide range of technical, economical and environmental application areas. However, the verification of validity of such data is necessary condition for any further analysis. Correctness of the data can be proven with respect to various criteria, mainly the attention is focused on detecting possible outliers in the series. Among others, these comprise observations corrupted by failure of any measuring instrument or influence of other than the quantity of interest. In this contribution we present an advanced technique for time series outlier detection based on extreme value analysis. Extreme value theory is being successfully applied in many branches, and hence provides an adequate framework for detection of rare events such as outliers. The suitability of the method proposed is also discussed with respect to eventual automation of the whole procedure. The method was applied for validation of hourly air pollution data obtained in Brno, Czech Republic. The measurements were provided by automated instruments at locations with high traffic and industrial load. The proposed method might simplify the procedure of such extensive data verification.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10103 - Statistics and probability

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of 22nd International Conference on Soft Computing MENDEL 2016

ISBN

978-80-214-5365-4

ISSN

1803-3814

e-ISSN

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Počet stran výsledku

6

Strana od-do

255-260

Název nakladatele

Brno University of Technology

Místo vydání

Brno, Czech Republic

Místo konání akce

Brno

Datum konání akce

8. 6. 2016

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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