Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/23527
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Campo DC | Valor | Idioma |
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dc.contributor.author | Stepnicka, M. | - |
dc.contributor.author | Cortez, Paulo | - |
dc.contributor.author | Peralta Donate, Juan | - |
dc.contributor.author | Stepnickova, Lenka | - |
dc.date.accessioned | 2013-03-26T14:23:05Z | - |
dc.date.available | 2013-03-26T14:23:05Z | - |
dc.date.issued | 2013-05 | - |
dc.identifier.issn | 0957-4174 | - |
dc.identifier.uri | https://hdl.handle.net/1822/23527 | - |
dc.description.abstract | Accurate time series forecasting is a key issue to support individual and or- ganizational 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 neu- ral networks, support vector machines and genuine linguistic fuzzy rules. Performance of the suggested methods is experimentally justified on sea- sonal time series from distinct domains on three forecasting horizons. The most important contribution 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. | por |
dc.description.sponsorship | The research was supported by the European Regional Development Fund in the IT4Innovations Centre of Excellence project (CZ.1.05/1.1.00/02.0070). Furthermore, we gratefully acknowledge partial support of the project KON- TAKT II - LH12229 of MSˇMT CˇR. | por |
dc.language.iso | eng | por |
dc.publisher | Elsevier 1 | por |
dc.rights | openAccess | por |
dc.subject | Time series | por |
dc.subject | Computational intelligence | por |
dc.subject | Neural networks | por |
dc.subject | Support vector machine | por |
dc.subject | Fuzzy rules | por |
dc.subject | Genetic algorithm | por |
dc.title | Forecasting seasonal time series with computational intelligence: on recent methods and the potential of their combinations | por |
dc.type | article | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | http://dx.doi.org/10.1016/j.eswa.2012.10.001 | por |
sdum.publicationstatus | published | por |
oaire.citationStartPage | 1981 | por |
oaire.citationEndPage | 1922 | por |
oaire.citationIssue | 6 | por |
oaire.citationTitle | Expert Systems with Applications | por |
oaire.citationVolume | 40 | por |
dc.identifier.doi | 10.1016/j.eswa.2012.10.001 | por |
dc.subject.wos | Science & Technology | por |
sdum.journal | Expert Systems with Applications | por |
Aparece nas coleções: | CAlg - Artigos em revistas internacionais / Papers in international journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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CI_TS_v16.pdf | Documento principal | 443,84 kB | Adobe PDF | Ver/Abrir |