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

Gutiérrez de los Rios, Luis Manuel. (2021). General drawbacks in Deep Learning for COVID-19 Time Series Forecasting Master Thesis, Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial

Ficheros (Some files may be inaccessible until you login with your e-spacio credentials)
Nombre Descripción Tipo MIME Size
Gutierrez_DeLosRios_LuisManuel_TFM.pdf Gutierrez_DeLosRios_LuisManuel_TFM.pdf application/pdf 1.00MB

Título General drawbacks in Deep Learning for COVID-19 Time Series Forecasting
Autor(es) Gutiérrez de los Rios, Luis Manuel
Abstract 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.
Notas adicionales Trabajo de Fin de Máster. Máster Universitario en I.A. Avanzada: Fundamentos, Métodos y Aplicaciones. UNED
Materia(s) Ingeniería Informática
Editor(es) Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial
Director/Tutor Aznarte Mellado, José Luis
Fecha 2021-09-19
Formato application/pdf
Identificador bibliuned:master-ETSInformatica-IAA-Lmgutierrez
http://e-spacio.uned.es/fez/view/bibliuned:master-ETSInformatica-IAA-Lmgutierrez
Idioma eng
Versión de la publicación acceptedVersion
Nivel de acceso y licencia http://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
Tipo de recurso master Thesis
Tipo de acceso Acceso abierto

 
Versiones
Versión Tipo de filtro
Contador de citas: Google Scholar Search Google Scholar
Estadísticas de acceso: 167 Visitas, 60 Descargas  -  Estadísticas en detalle
Creado: Fri, 30 Sep 2022, 19:06:11 CET