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

Bibliographic Details
Main Author: Ashofteh, Afshin
Publication Date: 2021
Other Authors: Bravo, Jorge Miguel, Ayuso, Mercedes
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/138275
Summary: Ashofteh, 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.
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spelling A Novel Layered Learning Approach for Forecasting Respiratory Disease Excess Mortality during the COVID-19 pandemicTime Series methodMachine LearningEnsemble Bayesian Model Averaging (EBMA)ForecastingExcess MortalityInformation Systems and ManagementManagement Information SystemsManagement of Technology and InnovationInformation SystemsComputer Science ApplicationsAshofteh, 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.Forecasting 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.APSI - Associação Portuguesa de Sistemas de InformaçãoNOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNAshofteh, AfshinBravo, Jorge MiguelAyuso, Mercedes2022-05-19T22:17:09Z20212021-01-01T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersion19application/pdfhttp://hdl.handle.net/10362/138275engPURE: 32546560info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-11-18T01:40:42Zoai:run.unl.pt:10362/138275Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:32:29.784143Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv A Novel Layered Learning Approach for Forecasting Respiratory Disease Excess Mortality during the COVID-19 pandemic
title A Novel Layered Learning Approach for Forecasting Respiratory Disease Excess Mortality during the COVID-19 pandemic
spellingShingle A Novel Layered Learning Approach for Forecasting Respiratory Disease Excess Mortality during the COVID-19 pandemic
Ashofteh, Afshin
Time Series method
Machine Learning
Ensemble Bayesian Model Averaging (EBMA)
Forecasting
Excess Mortality
Information Systems and Management
Management Information Systems
Management of Technology and Innovation
Information Systems
Computer Science Applications
title_short A Novel Layered Learning Approach for Forecasting Respiratory Disease Excess Mortality during the COVID-19 pandemic
title_full A Novel Layered Learning Approach for Forecasting Respiratory Disease Excess Mortality during the COVID-19 pandemic
title_fullStr A Novel Layered Learning Approach for Forecasting Respiratory Disease Excess Mortality during the COVID-19 pandemic
title_full_unstemmed A Novel Layered Learning Approach for Forecasting Respiratory Disease Excess Mortality during the COVID-19 pandemic
title_sort A Novel Layered Learning Approach for Forecasting Respiratory Disease Excess Mortality during the COVID-19 pandemic
author Ashofteh, Afshin
author_facet Ashofteh, Afshin
Bravo, Jorge Miguel
Ayuso, Mercedes
author_role author
author2 Bravo, Jorge Miguel
Ayuso, Mercedes
author2_role author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Ashofteh, Afshin
Bravo, Jorge Miguel
Ayuso, Mercedes
dc.subject.por.fl_str_mv Time Series method
Machine Learning
Ensemble Bayesian Model Averaging (EBMA)
Forecasting
Excess Mortality
Information Systems and Management
Management Information Systems
Management of Technology and Innovation
Information Systems
Computer Science Applications
topic Time Series method
Machine Learning
Ensemble Bayesian Model Averaging (EBMA)
Forecasting
Excess Mortality
Information Systems and Management
Management Information Systems
Management of Technology and Innovation
Information Systems
Computer Science Applications
description Ashofteh, 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.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-01-01T00:00:00Z
2022-05-19T22:17:09Z
dc.type.driver.fl_str_mv conference object
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/138275
url http://hdl.handle.net/10362/138275
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv PURE: 32546560
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 19
application/pdf
dc.publisher.none.fl_str_mv APSI - Associação Portuguesa de Sistemas de Informação
publisher.none.fl_str_mv APSI - Associação Portuguesa de Sistemas de Informação
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833596773505957888

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