About: Forecasting seasonal time series with computational intelligence: on recent methods and the potential of their combinations     Goto   Sponge   NotDistinct   Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

AttributesValues
rdf:type
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. (en)
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 (en)
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 (en)
skos:notation
  • RIV/61988987:17610/13:A13012G0!RIV13-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), S
http://linked.open...iv/cisloPeriodika
  • 6
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
  • 75431
http://linked.open...ai/riv/idVysledku
  • RIV/61988987:17610/13:A13012G0
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Time series; Computational intelligence; Neural networks; Support vector machine; Fuzzy rules \sep Genetic algorithm (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • NL - Nizozemsko
http://linked.open...ontrolniKodProRIV
  • [2015CB6E3560]
http://linked.open...i/riv/nazevZdroje
  • EXPERT SYST APPL
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...v/svazekPeriodika
  • 40
http://linked.open...iv/tvurceVysledku
  • Štěpnička, Martin
  • Cortez, Paulo
  • Peralta Donate, Juan
  • Štěpničková, Lenka
issn
  • 0957-4174
number of pages
http://localhost/t...ganizacniJednotka
  • 17610
Faceted Search & Find service v1.16.118 as of Jun 21 2024


Alternative Linked Data Documents: ODE     Content Formats:   [cxml] [csv]     RDF   [text] [turtle] [ld+json] [rdf+json] [rdf+xml]     ODATA   [atom+xml] [odata+json]     Microdata   [microdata+json] [html]    About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data] Valid XHTML + RDFa
OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 31 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software