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Title:Parametrični in neparametrični pristopi za odkrivanje trenda v časovnih vrstah
Authors:ID Kraner Šumenjak, Tadeja (Author)
ID Sem, Vilma (Author)
Files:.pdf Acta_agriculturae_Slovenica_2011_Kraner_Sumenjak,_Sustar_Parametricni_in_neparametricni_pristopi_za_odkrivanje_trenda_v_casovnih_vrstah.pdf (394,47 KB)
MD5: 573727DF4CD8A6A0526FF6653D084AED
 
URL http://aas.bf.uni-lj.si/september2011/16kraner.pdf
 
Language:Slovenian
Work type:Scientific work
Typology:1.02 - Review Article
Organization:FKBV - Faculty of Agriculture and Life Sciences
Abstract:Eno od najpogosteje uporabljenih orodij za odkrivanje sprememb v časovnih vrstah je analiza trenda. Obstaja veliko parametričnih in neparametričnih testov za odkrivanje za odkrivanje značilnih trendov v časovnih vrstah. Slednji se pogosteje uporabljajo zaradi manjšega števila predpostavk potrebnihza njihovo izvedbo. Najpogosteje uporabljen test za odkrivanje značilnih trendov je Mann-Kendallov test, ki še vedno zahteva, da so vzorčni podatki neodvisni. Za odstranitev vpliva serialne korelacije v Mann-Kendallovem testu so bili vpeljani različni popravki in metode pred-beljenja. V članku je pregled najpogosteje uporabljenih pristopov za odkrivanje trenda v časovnih vrstah ob prisotnosti serialne korelacije ali brez nje. Na koncu so te metode uporabljene še na realnih podatkih.
Keywords:analiza trenda, metoda najmanjših kvadratov, Mann-Kendallov test, korelacijski koeficient, avtokorelacija, pred-beljenje
Publication status:Published
Publication version:Version of Record
Year of publishing:2011
Number of pages:str. 305-312
Numbering:Letn. 97, št. 3
PID:20.500.12556/DKUM-52195 New window
ISSN:1581-9175
UDC:311
ISSN on article:1581-9175
COBISS.SI-ID:3230764 New window
NUK URN:URN:SI:UM:DK:OJJTKYPI
Publication date in DKUM:10.07.2015
Views:1961
Downloads:274
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Categories:Misc.
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Record is a part of a journal

Title:Acta agriculturae Slovenica
Shortened title:Acta agric. Slov.
Publisher:Biotehniška fakulteta Univerze v Ljubljani
ISSN:1581-9175
COBISS.SI-ID:213840640 New window

Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:10.07.2015

Secondary language

Language:English
Title:Parametric and nonparametric aproach for trend detection in times series
Abstract:One of the most commonly used tools for detecting changes in time series is trend analysis. A number of parametric and nonparametric tests exist to detect the significance of trends in time series. The latter have been widely used mainly because of fewer number of assumptions needed in their implementation. The most often used test for detecting significant trends is Mann-Kendall test, that still requires sample data to be serially independent. To eliminate the effect of serial correlation on the Man-Kendall test different correction and pre-whitening methods have been introduced. This paper reviews the most commonly used approaches for trend detection in time series with or without presence of serial correlation. At the end these methods are applied to real datasets.
Keywords:trend analysis, least square method, Mann-Kendall test, correlation coefficient, autocorrelation, pre-whitening


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