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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: Progresses with Associations Mining
  • Fuzzy Rule-Based Ensemble for Time Series Prediction: Progresses with Associations Mining (en)
skos:prefLabel
  • Fuzzy Rule-Based Ensemble for Time Series Prediction: Progresses with Associations Mining
  • Fuzzy Rule-Based Ensemble for Time Series Prediction: Progresses with Associations Mining (en)
skos:notation
  • RIV/61988987:17610/15:A1501B27!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
  • 164
http://linked.open...ai/riv/idVysledku
  • RIV/61988987:17610/15:A1501B27
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Fuzzy rule-based ensemble; time series; fuzzy rules; ensemble; perception-based logical deduction; linguistic associations mining (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [7E7797B0AE8F]
http://linked.open...v/mistoKonaniAkce
  • Warsaw
http://linked.open...i/riv/mistoVydani
  • Heidelberg
http://linked.open...i/riv/nazevZdroje
  • Strengthening Links between Data Analysis and Soft Computing
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
  • 2194-5357
number of pages
http://purl.org/ne...btex#hasPublisher
  • Springer-Verlag
https://schema.org/isbn
  • 978-3-319-10764-6
http://localhost/t...ganizacniJednotka
  • 17610
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