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Statements

Subject Item
n2:RIV%2F61988987%3A17610%2F13%3AA13012G0%21RIV13-MSM-17610___
rdf:type
skos:Concept n15:Vysledek
dcterms:description
Accurate time series forecasting is a key issue to support individual and organizational decision making. In this paper, we introduce novel methods for multi-step seasonal time series forecasting. All the presented methods stem from computational intelligence techniques: evolutionary artificial neural networks, support vector machines and genuine linguistic fuzzy rules. Performance of the suggested methods is experimentally justified on seasonal time series from distinct domains on three forecasting horizons. The most important contribution is is the introduction of a new hybrid combination using linguistic fuzzy rules and the other computational intelligence methods. This hybrid combination presents competitive forecasts, when compared with the popular ARIMA method. Moreover, such hybrid model is more easy to interpret by decision-makers when modeling trended series. Accurate time series forecasting is a key issue to support individual and organizational decision making. In this paper, we introduce novel methods for multi-step seasonal time series forecasting. All the presented methods stem from computational intelligence techniques: evolutionary artificial neural networks, support vector machines and genuine linguistic fuzzy rules. Performance of the suggested methods is experimentally justified on seasonal time series from distinct domains on three forecasting horizons. The most important contribution is is the introduction of a new hybrid combination using linguistic fuzzy rules and the other computational intelligence methods. This hybrid combination presents competitive forecasts, when compared with the popular ARIMA method. Moreover, such hybrid model is more easy to interpret by decision-makers when modeling trended series.
dcterms:title
Forecasting seasonal time series with computational intelligence: on recent methods and the potential of their combinations Forecasting seasonal time series with computational intelligence: on recent methods and the potential of their combinations
skos:prefLabel
Forecasting seasonal time series with computational intelligence: on recent methods and the potential of their combinations Forecasting seasonal time series with computational intelligence: on recent methods and the potential of their combinations
skos:notation
RIV/61988987:17610/13:A13012G0!RIV13-MSM-17610___
n15:predkladatel
n16:orjk%3A17610
n3:aktivita
n11:S n11:P
n3:aktivity
P(ED1.1.00/02.0070), P(LH12229), S
n3:cisloPeriodika
6
n3:dodaniDat
n14:2013
n3:domaciTvurceVysledku
n6:3721620 n6:6800327
n3:druhVysledku
n9:J
n3:duvernostUdaju
n4:S
n3:entitaPredkladatele
n18:predkladatel
n3:idSjednocenehoVysledku
75431
n3:idVysledku
RIV/61988987:17610/13:A13012G0
n3:jazykVysledku
n12:eng
n3:klicovaSlova
Time series; Computational intelligence; Neural networks; Support vector machine; Fuzzy rules \sep Genetic algorithm
n3:klicoveSlovo
n5:Computational%20intelligence n5:Support%20vector%20machine n5:Time%20series n5:Neural%20networks n5:Fuzzy%20rules%20%5Csep%20Genetic%20algorithm
n3:kodStatuVydavatele
NL - Nizozemsko
n3:kontrolniKodProRIV
[2015CB6E3560]
n3:nazevZdroje
EXPERT SYST APPL
n3:obor
n17:BA
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
4
n3:projekt
n8:ED1.1.00%2F02.0070 n8:LH12229
n3:rokUplatneniVysledku
n14:2013
n3:svazekPeriodika
40
n3:tvurceVysledku
Cortez, Paulo Štěpničková, Lenka Štěpnička, Martin Peralta Donate, Juan
s:issn
0957-4174
s:numberOfPages
12
n19:organizacniJednotka
17610