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

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dc.contributor.authorGutiérrez de los Rios, Luis Manuel
dc.contributor.directorAznarte Mellado, José Luis
dc.date.accessioned2024-05-20T12:34:51Z
dc.date.available2024-05-20T12:34:51Z
dc.date.issued2021-09-19
dc.description.abstractIn 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.en
dc.description.versionversión final
dc.identifier.urihttps://hdl.handle.net/20.500.14468/14552
dc.language.isoen
dc.publisherUniversidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.degreeMáster Universitario en I.A. Avanzada: Fundamentos, Métodos y Aplicaciones
dc.relation.departmentInteligencia Artificial
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.titleGeneral drawbacks in Deep Learning for COVID-19 Time Series Forecastinges
dc.typetesis de maestríaes
dc.typemaster thesisen
dspace.entity.typePublication
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