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A Novel Layered Learning Approach for Forecasting Respiratory Disease Excess Mortality during the COVID-19 pandemic

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dc.contributor.authorAshofteh, Afshin
dc.contributor.authorBravo, Jorge Miguel
dc.contributor.authorAyuso, Mercedes
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.date.accessioned2022-05-19T22:17:09Z
dc.date.available2022-05-19T22:17:09Z
dc.date.issued2021
dc.descriptionAshofteh, A., Bravo, J. M., & Ayuso, M. (2021). A Novel Layered Learning Approach for Forecasting Respiratory Disease Excess Mortality during the COVID-19 pandemic. 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".[21th Portuguese Association of Information Systems Conference] (pp. 1-18). Associação Portuguesa de Sistemas de Informação. ----- The authors are grateful to the anonymous reviewers for their constructive comments. Their critical and constructive remarks were precious to improve the final paper. Jorge M. Bravo was supported by Portuguese national science funds through FCT under the project UIDB/04152/2020-Centro de Investigação em Gestão de Informação (MagIC). Additionally, M. Ayuso is grateful to the Secretaria d’Universitats i Recerca del departament d’Empresa i Coneixement de la Generalitat de Catalunya for funding received under grant 2020-PANDE-00074. It’s a research project directly related to COVID and economy.
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.extent19
dc.format.extent753920
dc.identifier.otherPURE: 32546560
dc.identifier.otherPURE UUID: 13de1216-1c93-440b-84ed-cba19ef40002
dc.identifier.otherScopus: 85139920224
dc.identifier.otherORCID: /0000-0002-7389-5103/work/113324155
dc.identifier.urihttp://hdl.handle.net/10362/138275
dc.identifier.urlhttps://www.scopus.com/pages/publications/85139920224
dc.language.isoeng
dc.peerreviewedyes
dc.publisherAPSI - Associação Portuguesa de Sistemas de Informação
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
dc.relationInformation Management Research Center
dc.subjectTime Series method
dc.subjectMachine Learning
dc.subjectEnsemble Bayesian Model Averaging (EBMA)
dc.subjectForecasting
dc.subjectExcess Mortality
dc.subjectInformation Systems and Management
dc.subjectManagement Information Systems
dc.subjectManagement of Technology and Innovation
dc.subjectInformation Systems
dc.subjectComputer Science Applications
dc.titleA Novel Layered Learning Approach for Forecasting Respiratory Disease Excess Mortality during the COVID-19 pandemicen
dc.typeconference object
degois.publication.firstPage1
degois.publication.lastPage18
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
oaire.awardNumberUIDB/04152/2020
oaire.awardTitleInformation Management Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication3274bdb3-4dd3-4bbe-8f74-d34190081f87
relation.isProjectOfPublication.latestForDiscovery3274bdb3-4dd3-4bbe-8f74-d34190081f87

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