Using N-BEATS ensembles to predict automated guided vehicle deviation

Detalles Bibliográficos
Autores: Karamchandani Batra, Amit|||0000-0002-0311-6610, Mozo Velasco, Bonifacio Alberto|||0000-0001-9743-8604, Vakaruk, Stanislav|||0000-0003-4263-0206, Gómez Canaval, Sandra María|||0000-0002-9757-7871, Sierra García, Jesús Enrique|||0000-0001-6088-9954, Pastor Gutiérrez, Antonio|||0000-0002-6937-1585
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universidad Politécnica de Madrid
Repositorio:Archivo Digital UPM
OAI Identifier:oai:oa.upm.es:81106
Acceso en línea:https://oa.upm.es/81106/
Access Level:acceso abierto
Palabra clave:5G
Industrial automated guided vehicles
Maintenance
multi-access edge computing
Time-series forecasting
deep learning
Multi-access edge computing
Time
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network_name_str España
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spelling Using N-BEATS ensembles to predict automated guided vehicle deviationKaramchandani Batra, Amit|||0000-0002-0311-6610Mozo Velasco, Bonifacio Alberto|||0000-0001-9743-8604Vakaruk, Stanislav|||0000-0003-4263-0206Gómez Canaval, Sandra María|||0000-0002-9757-7871Sierra García, Jesús Enrique|||0000-0001-6088-9954Pastor Gutiérrez, Antonio|||0000-0002-6937-15855GIndustrial automated guided vehiclesMaintenancemulti-access edge computingTime-series forecasting5Gdeep learningIndustrial automated guided vehiclesMulti-access edge computingTimeTime-series forecasting20232023-11-01journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articlehttps://oa.upm.es/81106/reponame:Archivo Digital UPMinstname:Universidad Politécnica de MadridInglésenEuropean Commission 10.13039/501100000780 Horizon 2020 Framework Programme 780732Universidad Politécnica de Madrid 10.13039/501100003759 RP2161220029open accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:oa.upm.es:811062026-06-21T12:45:07Z
dc.title.none.fl_str_mv Using N-BEATS ensembles to predict automated guided vehicle deviation
title Using N-BEATS ensembles to predict automated guided vehicle deviation
spellingShingle Using N-BEATS ensembles to predict automated guided vehicle deviation
Karamchandani Batra, Amit|||0000-0002-0311-6610
5G
Industrial automated guided vehicles
Maintenance
multi-access edge computing
Time-series forecasting
5G
deep learning
Industrial automated guided vehicles
Multi-access edge computing
Time
Time-series forecasting
title_short Using N-BEATS ensembles to predict automated guided vehicle deviation
title_full Using N-BEATS ensembles to predict automated guided vehicle deviation
title_fullStr Using N-BEATS ensembles to predict automated guided vehicle deviation
title_full_unstemmed Using N-BEATS ensembles to predict automated guided vehicle deviation
title_sort Using N-BEATS ensembles to predict automated guided vehicle deviation
dc.creator.none.fl_str_mv Karamchandani Batra, Amit|||0000-0002-0311-6610
Mozo Velasco, Bonifacio Alberto|||0000-0001-9743-8604
Vakaruk, Stanislav|||0000-0003-4263-0206
Gómez Canaval, Sandra María|||0000-0002-9757-7871
Sierra García, Jesús Enrique|||0000-0001-6088-9954
Pastor Gutiérrez, Antonio|||0000-0002-6937-1585
author Karamchandani Batra, Amit|||0000-0002-0311-6610
author_facet Karamchandani Batra, Amit|||0000-0002-0311-6610
Mozo Velasco, Bonifacio Alberto|||0000-0001-9743-8604
Vakaruk, Stanislav|||0000-0003-4263-0206
Gómez Canaval, Sandra María|||0000-0002-9757-7871
Sierra García, Jesús Enrique|||0000-0001-6088-9954
Pastor Gutiérrez, Antonio|||0000-0002-6937-1585
author_role author
author2 Mozo Velasco, Bonifacio Alberto|||0000-0001-9743-8604
Vakaruk, Stanislav|||0000-0003-4263-0206
Gómez Canaval, Sandra María|||0000-0002-9757-7871
Sierra García, Jesús Enrique|||0000-0001-6088-9954
Pastor Gutiérrez, Antonio|||0000-0002-6937-1585
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv 5G
Industrial automated guided vehicles
Maintenance
multi-access edge computing
Time-series forecasting
5G
deep learning
Industrial automated guided vehicles
Multi-access edge computing
Time
Time-series forecasting
topic 5G
Industrial automated guided vehicles
Maintenance
multi-access edge computing
Time-series forecasting
5G
deep learning
Industrial automated guided vehicles
Multi-access edge computing
Time
Time-series forecasting
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-11-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://oa.upm.es/81106/
url https://oa.upm.es/81106/
dc.language.none.fl_str_mv Inglés
en
language_invalid_str_mv Inglés
en
dc.relation.none.fl_str_mv European Commission 10.13039/501100000780 Horizon 2020 Framework Programme 780732
Universidad Politécnica de Madrid 10.13039/501100003759 RP2161220029
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Archivo Digital UPM
instname:Universidad Politécnica de Madrid
instname_str Universidad Politécnica de Madrid
reponame_str Archivo Digital UPM
collection Archivo Digital UPM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300724

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