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Statements

Subject Item
n2:RIV%2F61988987%3A17610%2F13%3AA14017T8%21RIV14-MSM-17610___
rdf:type
skos:Concept n18:Vysledek
dcterms:description
There are many various methods to forecast time series. However, there is no single forecasting method that generally outperforms any other. Consequently, 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 are being proposed. These techniques combine more individual forecasting methods. In this contribution, we employ the so called fuzzy rule-based ensemble to determine the weights 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. There are many various methods to forecast time series. However, there is no single forecasting method that generally outperforms any other. Consequently, 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 are being proposed. These techniques combine more individual forecasting methods. In this contribution, we employ the so called fuzzy rule-based ensemble to determine the weights 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 with use of linguistic associations mining for time series prediction Fuzzy rule-based ensemble with use of linguistic associations mining for time series prediction
skos:prefLabel
Fuzzy rule-based ensemble with use of linguistic associations mining for time series prediction Fuzzy rule-based ensemble with use of linguistic associations mining for time series prediction
skos:notation
RIV/61988987:17610/13:A14017T8!RIV14-MSM-17610___
n18:predkladatel
n19:orjk%3A17610
n3:aktivita
n13:P
n3:aktivity
P(ED1.1.00/02.0070), P(LH12229)
n3:dodaniDat
n10:2014
n3:domaciTvurceVysledku
n9:3721620 n9:6800327 n9:3252183
n3:druhVysledku
n8:D
n3:duvernostUdaju
n16:S
n3:entitaPredkladatele
n15:predkladatel
n3:idSjednocenehoVysledku
76097
n3:idVysledku
RIV/61988987:17610/13:A14017T8
n3:jazykVysledku
n7:eng
n3:klicovaSlova
Time series; fuzzy rules; ensembles; Fuzzy Rule Based Ensemble; fuzzy GUHA; linguistic associations; perception-based logical deduction
n3:klicoveSlovo
n4:linguistic%20associations n4:fuzzy%20rules n4:fuzzy%20GUHA n4:perception-based%20logical%20deduction n4:ensembles n4:Time%20series n4:Fuzzy%20Rule%20Based%20Ensemble
n3:kontrolniKodProRIV
[F9C25CD61DBD]
n3:mistoKonaniAkce
Milano
n3:nazevZdroje
Proceedings of the 8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT)
n3:obor
n21:BA
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n6:LH12229 n6:ED1.1.00%2F02.0070
n3:rokUplatneniVysledku
n10:2013
n3:tvurceVysledku
Štěpnička, Martin Štěpničková, Lenka Sikora, David
n3:typAkce
n17:WRD
n3:wos
000327668700063
n3:zahajeniAkce
2013-01-01+01:00
s:numberOfPages
8
n12:hasPublisher
Atlantis Press
n22:isbn
9789078677789
n20:organizacniJednotka
17610