Extreme learning machine ensemble model for time series forecasting boosted by PSO: Application to an electric consumption problem

Ensemble Model is a tool that has attracted attention due to its capability to improve the outcome performance of emerging techniques to solve the modelling and classifying problem. However, the feasibility of applying intelligent algorithms to build the Ensemble Model presents a challenge of its ow...

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Autores: Larrea Sukia, Mikel, Porto, Alain, Irigoyen Gordo, Eloy, Barragán Piña, Antonio Javier, Andújar Márquez, José Manuel
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/65345
Acceso en línea:http://hdl.handle.net/10810/65345
Access Level:acceso abierto
Palabra clave:ensemble
ELM
PSO
time-series
electric consumption forecasting
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spelling Extreme learning machine ensemble model for time series forecasting boosted by PSO: Application to an electric consumption problemLarrea Sukia, MikelPorto, AlainIrigoyen Gordo, EloyBarragán Piña, Antonio JavierAndújar Márquez, José ManuelensembleELMPSOtime-serieselectric consumption forecastingEnsemble Model is a tool that has attracted attention due to its capability to improve the outcome performance of emerging techniques to solve the modelling and classifying problem. However, the feasibility of applying intelligent algorithms to build the Ensemble Model presents a challenge of its own. In this work, an Extreme Learning Machine ensemble is applied to the Time Series modelling problem. We develop a thorough study of the models that will be candidates to compose the ensemble, obtaining statistical results of optimal topologies to solve each Time Series problem. The proposed method for the ensemble is the weighted averaging method, where the parameters (weights) are tuned with the Particle Swarm Optimization algorithm. Lastly, the ensemble is tested first in the well known Santa Fe Time Series Competition benchmark. Given the obtained satisfactory results, the ensemble is secondly tested in a real problem of Spain’s electric consumption forecasting. It is demonstrated that the PSO is a suitable algorithm to optimize Extreme Learning Machine ensemble and that the proposed strategy can obtain good results in both Time Series problems.This work comes under the framework of the project IT1284-19 granted by the Regional Government of the Basque Country. The authors would like to thank the company IK4-IDEKO that has supported this work.Elsevier202420242021info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/65345reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoIngléshttps://www.sciencedirect.com/science/article/pii/S0925231220316544info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/3.0/es/© 2020 Elsevier B.V. under CC BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/)Atribución-NoComercial-CompartirIgual 3.0 Españaoai:addi.ehu.eus:10810/653452026-06-18T09:23:17Z
dc.title.none.fl_str_mv Extreme learning machine ensemble model for time series forecasting boosted by PSO: Application to an electric consumption problem
title Extreme learning machine ensemble model for time series forecasting boosted by PSO: Application to an electric consumption problem
spellingShingle Extreme learning machine ensemble model for time series forecasting boosted by PSO: Application to an electric consumption problem
Larrea Sukia, Mikel
ensemble
ELM
PSO
time-series
electric consumption forecasting
title_short Extreme learning machine ensemble model for time series forecasting boosted by PSO: Application to an electric consumption problem
title_full Extreme learning machine ensemble model for time series forecasting boosted by PSO: Application to an electric consumption problem
title_fullStr Extreme learning machine ensemble model for time series forecasting boosted by PSO: Application to an electric consumption problem
title_full_unstemmed Extreme learning machine ensemble model for time series forecasting boosted by PSO: Application to an electric consumption problem
title_sort Extreme learning machine ensemble model for time series forecasting boosted by PSO: Application to an electric consumption problem
dc.creator.none.fl_str_mv Larrea Sukia, Mikel
Porto, Alain
Irigoyen Gordo, Eloy
Barragán Piña, Antonio Javier
Andújar Márquez, José Manuel
author Larrea Sukia, Mikel
author_facet Larrea Sukia, Mikel
Porto, Alain
Irigoyen Gordo, Eloy
Barragán Piña, Antonio Javier
Andújar Márquez, José Manuel
author_role author
author2 Porto, Alain
Irigoyen Gordo, Eloy
Barragán Piña, Antonio Javier
Andújar Márquez, José Manuel
author2_role author
author
author
author
dc.subject.none.fl_str_mv ensemble
ELM
PSO
time-series
electric consumption forecasting
topic ensemble
ELM
PSO
time-series
electric consumption forecasting
description Ensemble Model is a tool that has attracted attention due to its capability to improve the outcome performance of emerging techniques to solve the modelling and classifying problem. However, the feasibility of applying intelligent algorithms to build the Ensemble Model presents a challenge of its own. In this work, an Extreme Learning Machine ensemble is applied to the Time Series modelling problem. We develop a thorough study of the models that will be candidates to compose the ensemble, obtaining statistical results of optimal topologies to solve each Time Series problem. The proposed method for the ensemble is the weighted averaging method, where the parameters (weights) are tuned with the Particle Swarm Optimization algorithm. Lastly, the ensemble is tested first in the well known Santa Fe Time Series Competition benchmark. Given the obtained satisfactory results, the ensemble is secondly tested in a real problem of Spain’s electric consumption forecasting. It is demonstrated that the PSO is a suitable algorithm to optimize Extreme Learning Machine ensemble and that the proposed strategy can obtain good results in both Time Series problems.
publishDate 2021
dc.date.none.fl_str_mv 2021
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/65345
url http://hdl.handle.net/10810/65345
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://www.sciencedirect.com/science/article/pii/S0925231220316544
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
© 2020 Elsevier B.V. under CC BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Atribución-NoComercial-CompartirIgual 3.0 España
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/3.0/es/
© 2020 Elsevier B.V. under CC BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Atribución-NoComercial-CompartirIgual 3.0 España
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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