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Complexity in forecasting and predictive models

dc.contributor.authorSalmeron, Jose L.
dc.contributor.authorCorreia, Marisol B.
dc.contributor.authorPalos-Sanchez, Pedro R.
dc.date.accessioned2019-07-12T09:34:30Z
dc.date.available2019-07-12T09:34:30Z
dc.date.issued2019
dc.description.abstractTe challenge of this special issue has been to know the state of the problem related to forecasting modeling and the creation of a model to forecast the future behavior that supports decision making by supporting real-world applications. Tis issue has been highlighted by the quality of its research work on the critical importance of advanced analytical methods, such as neural networks, sof computing, evolutionary algorithms, chaotic models, cellular automata, agent-based models, and fnite mixture minimum squares (FIMIX-PLS).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1155/2019/8160659pt_PT
dc.identifier.issn1076-2787
dc.identifier.urihttp://hdl.handle.net/10400.1/12651
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherHindawipt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.titleComplexity in forecasting and predictive modelspt_PT
dc.typeother
dspace.entity.typePublication
oaire.citation.endPage3pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleComplexitypt_PT
oaire.citation.volume2019pt_PT
person.familyNameCorreia
person.givenNameMarisol B.
person.identifier.ciencia-idFE15-819A-4535
person.identifier.orcid0000-0002-1788-6114
person.identifier.scopus-author-id55333058000
rcaap.rightsopenAccesspt_PT
rcaap.typeotherpt_PT
relation.isAuthorOfPublicationee01864e-dec4-4285-a4b7-4ccb53d9afaa
relation.isAuthorOfPublication.latestForDiscoveryee01864e-dec4-4285-a4b7-4ccb53d9afaa

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