Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches

In this study, confirmed COVID-19 cases of Denmark, Belgium, Germany, France, United Kingdom, Finland, Switzerland and Turkey were modeled with Auto-Regressive Integrated Moving Average (ARIMA), Nonlinear Autoregression Neural Network (NARNN) and Long-Short Term Memory (LSTM) approaches. Six model p...

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Autores principales: Kırbaş, İsmail, Sözen, Adnan, Tuncer, Azim Doğuş, Kazancıoğlu, Fikret Şinasi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7293493/
https://www.ncbi.nlm.nih.gov/pubmed/32565625
http://dx.doi.org/10.1016/j.chaos.2020.110015
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author Kırbaş, İsmail
Sözen, Adnan
Tuncer, Azim Doğuş
Kazancıoğlu, Fikret Şinasi
author_facet Kırbaş, İsmail
Sözen, Adnan
Tuncer, Azim Doğuş
Kazancıoğlu, Fikret Şinasi
author_sort Kırbaş, İsmail
collection PubMed
description In this study, confirmed COVID-19 cases of Denmark, Belgium, Germany, France, United Kingdom, Finland, Switzerland and Turkey were modeled with Auto-Regressive Integrated Moving Average (ARIMA), Nonlinear Autoregression Neural Network (NARNN) and Long-Short Term Memory (LSTM) approaches. Six model performance metric were used to select the most accurate model (MSE, PSNR, RMSE, NRMSE, MAPE and SMAPE). According to the results of the first step of the study, LSTM was found the most accurate model. In the second stage of the study, LSTM model was provided to make predictions in a 14-day perspective that is yet to be known. Results of the second step of the study shows that the total cumulative case increase rate is expected to decrease slightly in many countries.
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spelling pubmed-72934932020-06-14 Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches Kırbaş, İsmail Sözen, Adnan Tuncer, Azim Doğuş Kazancıoğlu, Fikret Şinasi Chaos Solitons Fractals Article In this study, confirmed COVID-19 cases of Denmark, Belgium, Germany, France, United Kingdom, Finland, Switzerland and Turkey were modeled with Auto-Regressive Integrated Moving Average (ARIMA), Nonlinear Autoregression Neural Network (NARNN) and Long-Short Term Memory (LSTM) approaches. Six model performance metric were used to select the most accurate model (MSE, PSNR, RMSE, NRMSE, MAPE and SMAPE). According to the results of the first step of the study, LSTM was found the most accurate model. In the second stage of the study, LSTM model was provided to make predictions in a 14-day perspective that is yet to be known. Results of the second step of the study shows that the total cumulative case increase rate is expected to decrease slightly in many countries. Elsevier Ltd. 2020-09 2020-06-13 /pmc/articles/PMC7293493/ /pubmed/32565625 http://dx.doi.org/10.1016/j.chaos.2020.110015 Text en © 2020 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Kırbaş, İsmail
Sözen, Adnan
Tuncer, Azim Doğuş
Kazancıoğlu, Fikret Şinasi
Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches
title Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches
title_full Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches
title_fullStr Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches
title_full_unstemmed Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches
title_short Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches
title_sort comparative analysis and forecasting of covid-19 cases in various european countries with arima, narnn and lstm approaches
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7293493/
https://www.ncbi.nlm.nih.gov/pubmed/32565625
http://dx.doi.org/10.1016/j.chaos.2020.110015
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