Time Series Forecasting by Generalized Regression Neural Networks Trained With Multiple Series

Time series forecasting plays a key role in many fields such as business, energy or environment. Traditionally, statistical or machine learning models for time series forecasting are trained with the historical values of the series to be forecast. Unfortunately, some time series are too short to sui...

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Detalles Bibliográficos
Autores: Martínez-del-Río, Francisco, Frías, María Pilar, Pérez-Godoy, María Dolores, Rivera-Rivas, Antonio Jesús
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Universidad de Jaén
Repositorio:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
OAI Identifier:oai:ruja.ujaen.es:10953/6144
Acceso en línea:https://ieeexplore.ieee.org/abstract/document/9668960
https://hdl.handle.net/10953/6144
Access Level:acceso abierto
Palabra clave:Time series forecasting
Generalized regression neural networks
Model combination
004
311
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spelling Time Series Forecasting by Generalized Regression Neural Networks Trained With Multiple SeriesMartínez-del-Río, FranciscoFrías, María PilarPérez-Godoy, María DoloresRivera-Rivas, Antonio JesúsTime series forecastingGeneralized regression neural networksModel combination004311Time series forecasting plays a key role in many fields such as business, energy or environment. Traditionally, statistical or machine learning models for time series forecasting are trained with the historical values of the series to be forecast. Unfortunately, some time series are too short to suitably train a model. Motivated by this fact, this paper explores the use of data available in a pool or collection of time series to train a model that predicts an individual series. Concretely, we train a generalized regression neural network with the examples drawn from the historical values of a pool of series and then use the model to forecast individual series. In this sense several approaches are proposed, including to draw the examples from a pool of series related to the series to be forecast or the training of several models with mutually exclusive series and the combination of their forecasts. Experimental results in terms of forecasting accuracy using generalized regression neural networks are promising. Furthermore, the proposed approaches allow to forecast series that are too short to build a traditional generalized regression neural network model.Proyecto PID2019-107793GB-I00 del ministerio de ciencia, innovación y universidades de España.IEEE202520252022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://ieeexplore.ieee.org/abstract/document/9668960https://hdl.handle.net/10953/6144reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaéninstname:Universidad de JaénInglésIEEE Access 2022; vol. 10:3275-3283Attribution 3.0 Spainhttp://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:ruja.ujaen.es:10953/61442026-06-24T12:41:07Z
dc.title.none.fl_str_mv Time Series Forecasting by Generalized Regression Neural Networks Trained With Multiple Series
title Time Series Forecasting by Generalized Regression Neural Networks Trained With Multiple Series
spellingShingle Time Series Forecasting by Generalized Regression Neural Networks Trained With Multiple Series
Martínez-del-Río, Francisco
Time series forecasting
Generalized regression neural networks
Model combination
004
311
title_short Time Series Forecasting by Generalized Regression Neural Networks Trained With Multiple Series
title_full Time Series Forecasting by Generalized Regression Neural Networks Trained With Multiple Series
title_fullStr Time Series Forecasting by Generalized Regression Neural Networks Trained With Multiple Series
title_full_unstemmed Time Series Forecasting by Generalized Regression Neural Networks Trained With Multiple Series
title_sort Time Series Forecasting by Generalized Regression Neural Networks Trained With Multiple Series
dc.creator.none.fl_str_mv Martínez-del-Río, Francisco
Frías, María Pilar
Pérez-Godoy, María Dolores
Rivera-Rivas, Antonio Jesús
author Martínez-del-Río, Francisco
author_facet Martínez-del-Río, Francisco
Frías, María Pilar
Pérez-Godoy, María Dolores
Rivera-Rivas, Antonio Jesús
author_role author
author2 Frías, María Pilar
Pérez-Godoy, María Dolores
Rivera-Rivas, Antonio Jesús
author2_role author
author
author
dc.subject.none.fl_str_mv Time series forecasting
Generalized regression neural networks
Model combination
004
311
topic Time series forecasting
Generalized regression neural networks
Model combination
004
311
description Time series forecasting plays a key role in many fields such as business, energy or environment. Traditionally, statistical or machine learning models for time series forecasting are trained with the historical values of the series to be forecast. Unfortunately, some time series are too short to suitably train a model. Motivated by this fact, this paper explores the use of data available in a pool or collection of time series to train a model that predicts an individual series. Concretely, we train a generalized regression neural network with the examples drawn from the historical values of a pool of series and then use the model to forecast individual series. In this sense several approaches are proposed, including to draw the examples from a pool of series related to the series to be forecast or the training of several models with mutually exclusive series and the combination of their forecasts. Experimental results in terms of forecasting accuracy using generalized regression neural networks are promising. Furthermore, the proposed approaches allow to forecast series that are too short to build a traditional generalized regression neural network model.
publishDate 2022
dc.date.none.fl_str_mv 2022
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://ieeexplore.ieee.org/abstract/document/9668960
https://hdl.handle.net/10953/6144
url https://ieeexplore.ieee.org/abstract/document/9668960
https://hdl.handle.net/10953/6144
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv IEEE Access 2022; vol. 10:3275-3283
dc.rights.none.fl_str_mv Attribution 3.0 Spain
http://creativecommons.org/licenses/by/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 3.0 Spain
http://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
instname:Universidad de Jaén
instname_str Universidad de Jaén
reponame_str RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
collection RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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