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Interactivity to improve visual analysis in groups with different literacy levels

2021-12-01Documento de conferência Acesso aberto
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dc.contributor.authorOliveira, Monica
dc.contributor.authorCabral, Pedro
dc.contributor.authorTaibi, Davide
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.date.accessioned2022-06-28T22:14:41Z
dc.date.available2022-06-28T22:14:41Z
dc.date.issued2021-12-01
dc.descriptionOliveira, M., Cabral, P., & Taibi, D. (2021). Interactivity to improve visual analysis in groups with different literacy levels. In CAPSI 2021 Proceedings: 21ª Conferência da Associação Portuguesa de Sistemas de Informação, "Sociedade 5.0: Os desafios e as Oportunidades para os Sistemas de Informação (pp. 1-11). [21.ª Conferência da Associação Portuguesa de Sistemas de Informação (CAPSI’2021) 13 a 16 de outubro de 2021, Vila Real e Viseu, Portugal]. Associação Portuguesa de Sistemas de Informação. Link: https://aisel.aisnet.org/capsi2021/10. -----DATALIT has been funded as Knowledge Alliance under the Erasmus+ Program by European Commission. Project reference: 612561-EPP-1-2019-1-IT-EPPKA2-KA
dc.description.abstractForecasting model selection and model combination are the two contending approaches in the time series forecasting literature. Ensemble learning is useful for addressing a given predictive task by different predictive models when direct mapping from inputs to outputs is inaccurate. We adopt a layered learning approach to an ensemble learning strategy to solve the predictive tasks with improved predictive performance and take advantage of multiple learning processes into an ensemble model. In this proposed strategy, we build each model with a specific holdout and make the ensemble model of time series with a dynamic selection approach. For the experimental section, we studied more than twelve thousand observations in a portfolio of 61-time series of reported respiratory disease deaths to show the amount of improvement in predictive performance of excess mortality. Then we compare the forecasting outcome of our model with the corresponding total deaths of COVID-19 for selected countries.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent11
dc.format.extent314448
dc.identifier.otherPURE: 32644944
dc.identifier.otherPURE UUID: 748e435b-5451-41c8-96a9-a8c7b7603ec0
dc.identifier.otherScopus: 85139980122
dc.identifier.otherORCID: /0000-0001-8622-6008/work/115115818
dc.identifier.urihttp://hdl.handle.net/10362/140948
dc.identifier.urlhttps://www.scopus.com/pages/publications/85139980122
dc.identifier.urlhttps://aisel.aisnet.org/capsi2021/10
dc.language.isoeng
dc.peerreviewedyes
dc.publisherAPSI - Associação Portuguesa de Sistemas de Informação
dc.subjectData Visualization
dc.subjectData Literacy
dc.subjectInteractivity
dc.subjectInformation Systems and Management
dc.subjectManagement Information Systems
dc.subjectManagement of Technology and Innovation
dc.subjectInformation Systems
dc.subjectComputer Science Applications
dc.titleInteractivity to improve visual analysis in groups with different literacy levelsen
dc.typeconference object
degois.publication.firstPage1
degois.publication.lastPage11
degois.publication.titleCAPSI 2021 Proceedings
degois.publication.titleCAPSI 2021. 21ª Conferência da Associação Portuguesa de Sistemas de Informação, "Sociedade 5.0: Os desafios e as Oportunidades para os Sistemas de Informação"
dspace.entity.typePublication
rcaap.rightsopenAccess

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