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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. (en)
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 (en)
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 (en)
skos:notation
  • RIV/61988987:17610/13:A14017T8!RIV14-MSM-17610___
http://linked.open...avai/predkladatel
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(ED1.1.00/02.0070), P(LH12229)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 76097
http://linked.open...ai/riv/idVysledku
  • RIV/61988987:17610/13:A14017T8
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Time series; fuzzy rules; ensembles; Fuzzy Rule Based Ensemble; fuzzy GUHA; linguistic associations; perception-based logical deduction (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [F9C25CD61DBD]
http://linked.open...v/mistoKonaniAkce
  • Milano
http://linked.open...i/riv/nazevZdroje
  • Proceedings of the 8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT)
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Štěpnička, Martin
  • Štěpničková, Lenka
  • Sikora, David
http://linked.open...vavai/riv/typAkce
http://linked.open...ain/vavai/riv/wos
  • 000327668700063
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • Atlantis Press
https://schema.org/isbn
  • 9789078677789
http://localhost/t...ganizacniJednotka
  • 17610
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