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Statements

Subject Item
n2:RIV%2F61988987%3A17610%2F14%3AA1501B26%21RIV15-MSM-17610___
rdf:type
n13:Vysledek skos:Concept
dcterms:description
As there are many various methods for time series prediction developed but none of them generally outperforms all the others, there always exists a danger of choosing a method that is inappropriate for a given time series. To overcome such a problem, distinct ensemble techniques, that combine more individual forecasts, are being proposed. In this contribution, we employ the so called fuzzy rule-based ensemble. This method is constructed as a linear combination of a small number of forecasting methods where the weights of the combination are determined by fuzzy rule bases based on time series features such as trend, seasonality, or stationarity. For identification of fuzzy rule base, we use linguistic association mining. An exhaustive experimental justification is provided. As there are many various methods for time series prediction developed but none of them generally outperforms all the others, there always exists a danger of choosing a method that is inappropriate for a given time series. To overcome such a problem, distinct ensemble techniques, that combine more individual forecasts, are being proposed. In this contribution, we employ the so called fuzzy rule-based ensemble. This method is constructed as a linear combination of a small number of forecasting methods where the weights of the combination are determined by fuzzy rule bases based on time series features such as trend, seasonality, or stationarity. For identification of fuzzy rule base, we use linguistic association mining. An exhaustive experimental justification is provided.
dcterms:title
Fuzzy Rule-Based Ensemble for Time Series Prediction: The Application of Linguistic Associations Mining Fuzzy Rule-Based Ensemble for Time Series Prediction: The Application of Linguistic Associations Mining
skos:prefLabel
Fuzzy Rule-Based Ensemble for Time Series Prediction: The Application of Linguistic Associations Mining Fuzzy Rule-Based Ensemble for Time Series Prediction: The Application of Linguistic Associations Mining
skos:notation
RIV/61988987:17610/14:A1501B26!RIV15-MSM-17610___
n3:aktivita
n12:P
n3:aktivity
P(ED1.1.00/02.0070), P(LH12229)
n3:dodaniDat
n6:2015
n3:domaciTvurceVysledku
n4:6800327 n4:3721620 n4:3924971
n3:druhVysledku
n18:D
n3:duvernostUdaju
n7:S
n3:entitaPredkladatele
n20:predkladatel
n3:idSjednocenehoVysledku
17831
n3:idVysledku
RIV/61988987:17610/14:A1501B26
n3:jazykVysledku
n15:eng
n3:klicovaSlova
time series ensemble fuzzy association rules
n3:klicoveSlovo
n21:time%20series%20ensemble%20fuzzy%20association%20rules
n3:kontrolniKodProRIV
[A1F37C15DF0E]
n3:mistoKonaniAkce
Beijing, China
n3:mistoVydani
Beijing, China
n3:nazevZdroje
IEEE International Conference on Fuzzy Systems
n3:obor
n11:BA
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n16:LH12229 n16:ED1.1.00%2F02.0070
n3:rokUplatneniVysledku
n6:2014
n3:tvurceVysledku
Štěpničková, Lenka Burda, Michal Štěpnička, Martin
n3:typAkce
n19:WRD
n3:zahajeniAkce
2014-07-06+02:00
s:issn
1098-7584
s:numberOfPages
8
n17:hasPublisher
IEEE
n8:isbn
978-1-4799-2072-3
n14:organizacniJednotka
17610