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A Novel Layered Learning Approach for Forecasting Respiratory Disease Excess Mortality during the COVID-19 pandemic
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Orientador(es)
Resumo(s)
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.
Descrição
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.
Palavras-chave
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
Contexto Educativo
Citação
Editora
APSI - Associação Portuguesa de Sistemas de Informação