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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. (en)
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 (en)
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 (en)
skos:notation
  • RIV/61988987:17610/14:A1501B26!RIV15-MSM-17610___
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
  • 17831
http://linked.open...ai/riv/idVysledku
  • RIV/61988987:17610/14:A1501B26
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • time series ensemble fuzzy association rules (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [A1F37C15DF0E]
http://linked.open...v/mistoKonaniAkce
  • Beijing, China
http://linked.open...i/riv/mistoVydani
  • Beijing, China
http://linked.open...i/riv/nazevZdroje
  • IEEE International Conference on Fuzzy Systems
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
  • Burda, Michal
  • Štěpničková, Lenka
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
issn
  • 1098-7584
number of pages
http://purl.org/ne...btex#hasPublisher
  • IEEE
https://schema.org/isbn
  • 978-1-4799-2072-3
http://localhost/t...ganizacniJednotka
  • 17610
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