Interactivity to improve visual analysis in groups with different literacy levels

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Abstract

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.
Original languageEnglish
Title of host publicationCAPSI 2021 Proceedings
Subtitle of host publication 21ª Conferência da Associação Portuguesa de Sistemas de Informação, "Sociedade 5.0: Os desafios e as Oportunidades para os Sistemas de Informação
PublisherAssociação Portuguesa de Sistemas de Informação
Chapter10
Pages1-11
Number of pages11
Publication statusPublished - 1 Dec 2021
EventCAPSI 2021. 21ª Conferência da Associação Portuguesa de Sistemas de Informação, "Sociedade 5.0: Os desafios e as Oportunidades para os Sistemas de Informação" - , Portugal
Duration: 13 Oct 202116 Oct 2021
Conference number: 2021

Conference

ConferenceCAPSI 2021. 21ª Conferência da Associação Portuguesa de Sistemas de Informação, "Sociedade 5.0: Os desafios e as Oportunidades para os Sistemas de Informação"
Abbreviated titleCAPSI
Country/TerritoryPortugal
Period13/10/2116/10/21

Keywords

  • Data Visualization
  • Data Literacy
  • Interactivity

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