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Statements

Subject Item
n2:RIV%2F61988987%3A17610%2F13%3AA13014MF%21RIV13-MSM-17610___
rdf:type
skos:Concept n6:Vysledek
dcterms:description
There is no individual forecasting method that is generally for any given time series better than any other method. Thus, no matter the efficiency of a chosen method, there always exists a danger that for a given time series the chosen method is inappropriate. To overcome such a problem and avoid the above mentioned danger, distinct ensemble techniques that combine more individual forecasting methods are designed. These techniques basically construct a forecast as a linear combination of forecasts by individual methods. In this contribution, we construct a novel ensemble technique that determines the weights based on time series features. The protocol that carries a knowledge how to combine the individual forecasts is a fuzzy rule base (linguistic description). An exhaustive experimental justification is provided. The suggested ensemble approach based on fuzzy rules demonstrates both, lower forecasting error and higher robustness. There is no individual forecasting method that is generally for any given time series better than any other method. Thus, no matter the efficiency of a chosen method, there always exists a danger that for a given time series the chosen method is inappropriate. To overcome such a problem and avoid the above mentioned danger, distinct ensemble techniques that combine more individual forecasting methods are designed. These techniques basically construct a forecast as a linear combination of forecasts by individual methods. In this contribution, we construct a novel ensemble technique that determines the weights based on time series features. The protocol that carries a knowledge how to combine the individual forecasts is a fuzzy rule base (linguistic description). An exhaustive experimental justification is provided. The suggested ensemble approach based on fuzzy rules demonstrates both, lower forecasting error and higher robustness.
dcterms:title
Fuzzy Rule-Based Ensemble Forecasting: Introductory Study Fuzzy Rule-Based Ensemble Forecasting: Introductory Study
skos:prefLabel
Fuzzy Rule-Based Ensemble Forecasting: Introductory Study Fuzzy Rule-Based Ensemble Forecasting: Introductory Study
skos:notation
RIV/61988987:17610/13:A13014MF!RIV13-MSM-17610___
n6:predkladatel
n7:orjk%3A17610
n3:aktivita
n4:S n4:P
n3:aktivity
P(ED1.1.00/02.0070), P(LH12229), S
n3:dodaniDat
n9:2013
n3:domaciTvurceVysledku
n14:6800327 n14:3252183 n14:3721620
n3:druhVysledku
n11:D
n3:duvernostUdaju
n20:S
n3:entitaPredkladatele
n22:predkladatel
n3:idSjednocenehoVysledku
76096
n3:idVysledku
RIV/61988987:17610/13:A13014MF
n3:jazykVysledku
n12:eng
n3:klicovaSlova
Time series; Ensembles; Fuzzy rules
n3:klicoveSlovo
n17:Time%20series n17:Ensembles n17:Fuzzy%20rules
n3:kontrolniKodProRIV
[D21D41F7DC8A]
n3:mistoKonaniAkce
Konstanz
n3:mistoVydani
Heidelberg
n3:nazevZdroje
Synergies of Soft Computing and Statistics for Intelligent Data Analysis (Advances in Intelligent Systems and Computing))
n3:obor
n5:BA
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n15:LH12229 n15:ED1.1.00%2F02.0070
n3:rokUplatneniVysledku
n9:2013
n3:tvurceVysledku
Štěpnička, Martin Vavříčková, Lenka Sikora, David
n3:typAkce
n19:WRD
n3:wos
000312969600041
n3:zahajeniAkce
2012-01-01+01:00
s:numberOfPages
9
n8:hasPublisher
Springer-Verlag
n16:isbn
978-3-642-33041-4
n21:organizacniJednotka
17610