General drawbacks in Deep Learning for COVID-19 Time Series Forecasting

In the early stages of the COVID-19 breakdown, following the success of machine learning (ML) techniques, many researchers turned their efforts to predict the evolution of the global infection. In addition to classical statistical and machine learning trends, deep learning (DL) techniques are perfor...

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Detalles Bibliográficos
Autor: Gutiérrez de los Rios, Luis Manuel
Tipo de recurso: tesis de maestría
Fecha de publicación:2021
País:España
Institución:Universidad Nacional de Educación a Distancia
Repositorio:e-spacio. Repositorio Institucional de la UNED
Idioma:inglés
OAI Identifier:oai:e-spacio.uned.es:20.500.14468/14552
Acceso en línea:https://hdl.handle.net/20.500.14468/14552
Access Level:acceso abierto
Palabra clave:1203.04 Inteligencia artificial
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spelling General drawbacks in Deep Learning for COVID-19 Time Series ForecastingGutiérrez de los Rios, Luis Manuel1203.04 Inteligencia artificialIn the early stages of the COVID-19 breakdown, following the success of machine learning (ML) techniques, many researchers turned their efforts to predict the evolution of the global infection. In addition to classical statistical and machine learning trends, deep learning (DL) techniques are performing an important role in prediction and classifications tasks. These eforts resulted in a collection of models and applications, that were aimed to help health institutions to formulate and implement efective measures to prevent the spread of the pandemic. Nevertheless, as it will be shown here, this emergency research activity has not always been accompanied with a minimum level of quality, afecting replicability and reproducibility. This document pretends to provide an overview about the lights and shadows on the latest trends in this specifc area. Unlike previously released literature reviews, that are providing a wide overview about any type of AI techniques applied to overall aspects of the pandemics, this document will focus specifically on the use of DL techniques applied to COVID-19 time series forecasting. The production in this eld within the last months has become quite large. After setting a group of quality criteria, related to problem definition, dataset manipulation, model identification and evaluation, 96 papers has been screened. Most of the analysed papers did not meet the common quality standards of scienti fic work: none of them positively scored in all of the criteria, while only about one third scored positively in at least half of the defined criteria. The emergency character of this scientific production led to getting away from some of the basic requirements for quality scientific work.Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia ArtificialAznarte , José L.e-Spacio UNED20242024-05-2020212021-09-1920212021-09-19master thesishttp://purl.org/coar/resource_type/c_bdccinfo:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/20.500.14468/14552reponame:e-spacio. Repositorio Institucional de la UNEDinstname:Universidad Nacional de Educación a DistanciaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.esoai:e-spacio.uned.es:20.500.14468/145522026-06-06T12:38:31Z
dc.title.none.fl_str_mv General drawbacks in Deep Learning for COVID-19 Time Series Forecasting
title General drawbacks in Deep Learning for COVID-19 Time Series Forecasting
spellingShingle General drawbacks in Deep Learning for COVID-19 Time Series Forecasting
Gutiérrez de los Rios, Luis Manuel
1203.04 Inteligencia artificial
title_short General drawbacks in Deep Learning for COVID-19 Time Series Forecasting
title_full General drawbacks in Deep Learning for COVID-19 Time Series Forecasting
title_fullStr General drawbacks in Deep Learning for COVID-19 Time Series Forecasting
title_full_unstemmed General drawbacks in Deep Learning for COVID-19 Time Series Forecasting
title_sort General drawbacks in Deep Learning for COVID-19 Time Series Forecasting
dc.creator.none.fl_str_mv Gutiérrez de los Rios, Luis Manuel
author Gutiérrez de los Rios, Luis Manuel
author_facet Gutiérrez de los Rios, Luis Manuel
author_role author
dc.contributor.none.fl_str_mv Aznarte , José L.
e-Spacio UNED
dc.subject.none.fl_str_mv 1203.04 Inteligencia artificial
topic 1203.04 Inteligencia artificial
description In the early stages of the COVID-19 breakdown, following the success of machine learning (ML) techniques, many researchers turned their efforts to predict the evolution of the global infection. In addition to classical statistical and machine learning trends, deep learning (DL) techniques are performing an important role in prediction and classifications tasks. These eforts resulted in a collection of models and applications, that were aimed to help health institutions to formulate and implement efective measures to prevent the spread of the pandemic. Nevertheless, as it will be shown here, this emergency research activity has not always been accompanied with a minimum level of quality, afecting replicability and reproducibility. This document pretends to provide an overview about the lights and shadows on the latest trends in this specifc area. Unlike previously released literature reviews, that are providing a wide overview about any type of AI techniques applied to overall aspects of the pandemics, this document will focus specifically on the use of DL techniques applied to COVID-19 time series forecasting. The production in this eld within the last months has become quite large. After setting a group of quality criteria, related to problem definition, dataset manipulation, model identification and evaluation, 96 papers has been screened. Most of the analysed papers did not meet the common quality standards of scienti fic work: none of them positively scored in all of the criteria, while only about one third scored positively in at least half of the defined criteria. The emergency character of this scientific production led to getting away from some of the basic requirements for quality scientific work.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-09-19
2021
2021-09-19
2024
2024-05-20
dc.type.none.fl_str_mv master thesis
http://purl.org/coar/resource_type/c_bdcc
dc.type.openaire.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14468/14552
url https://hdl.handle.net/20.500.14468/14552
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
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info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial
publisher.none.fl_str_mv Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial
dc.source.none.fl_str_mv reponame:e-spacio. Repositorio Institucional de la UNED
instname:Universidad Nacional de Educación a Distancia
instname_str Universidad Nacional de Educación a Distancia
reponame_str e-spacio. Repositorio Institucional de la UNED
collection e-spacio. Repositorio Institucional de la UNED
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