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        • Universidad Jorge Tadeo Lozano (Colombia)
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        Deep Learning Methods for Forecasting COVID-19 Time-Series Data: A Comparative Study

        Registro en:
        0960-0779
        https://doi.org/10.1016/j.chaos.2020.110121
        http://hdl.handle.net/20.500.12010/11372
        https://doi.org/10.1016/j.chaos.2020.110121
        http://repositorioslatinoamericanos.uchile.cl/handle/2250/3497753
        Autor
        Zeroual, Abdelhafid
        Harrou, Fouzi
        Dairi, Abdelkader
        Sun, Ying
        Institución
        • Universidad Jorge Tadeo Lozano (Colombia)
        Resumen
        The novel coronavirus (COVID-19) has significantly spread over the world and comes up with new challenges to the research community. Although governments imposing numerous containment and social distancing measures, the need for the healthcare systems has dramatically increased and the effective management of infected patients becomes a challenging problem for hospitals. Thus, accurate short-term forecasting of the number of new contaminated and recovered cases is crucial for optimizing the available resources and arresting or slowing down the progression of such diseases. Recently, deep learning models demonstrated important improvements when handling time-series data in different applications. This paper presents a comparative study of five deep learning methods to forecast the number of new cases and recovered cases. Specifically, simple Recurrent Neural Network (RNN), Long short-term memory (LSTM), Bidirectional LSTM (BiLSTM), Gated recurrent units (GRUs) and Variational AutoEncoder (VAE) algorithms have been applied for global forecasting of COVID-19 cases based on a small volume of data. This study is based on daily confirmed and recovered cases collected from six countries namely Italy, Spain, France, China, USA, and Australia. Results demonstrate the promising potential of the deep learning model in forecasting COVID-19 cases and highlight the superior performance of the VAE compared to the other algorithms.
        Materias
        Data-driven
        Deep learning
        COVID-19
        Forecasting
        Gated recurrent units
        Long short-term memory
        Recurrent neural network
        Variational autoencoder

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        Red de Repositorios Latinoamericanos
        + de 8.000.000 publicaciones disponibles
        500 instituciones participantes
        Dirección de Servicios de Información y Bibliotecas (SISIB)
        Universidad de Chile
        Ingreso Administradores
        Colecciones destacadas
        • Tesis latinoamericanas
        • Tesis argentinas
        • Tesis chilenas
        • Tesis peruanas
        Nuevas incorporaciones
        • Argentina
        • Brasil
        • Colombia
        • México
        Dirección de Servicios de Información y Bibliotecas (SISIB)
        Universidad de Chile
        Red de Repositorios Latinoamericanos | 2006-2018