High roughness time series forecasting based on energy associated of series

Autores
Rodriguez Rivero, Cristian Maximiliano; Pucheta, Julián Antonio; Baumgartner, Josef Sylvester; Patiño, Héctor Daniel; Sauchelli, Victor Hugo
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this study, an algorithm to adjust parameters of high roughness time series based on energy associated of series using a feed-forward NN-based model is presented. The criterion for adjustment consists of building time series values from forecasted time series area and taking into account the roughness of series. These values are approximated by the NN to make a primitive calculated as an area by the predictor filter used as a new entrance. A comparison between this work and another that involves a similar approach to test time series prediction, indicates an improvement for certain sort of series. The NN filter output is intended to approximate the current value available from the series which has the same Hurst Parameter as the real time series. The proposed approach is tested over five time series obtained from samples of Mackey-Glass delay differential equations (MG). Therefore, these results show a model performance for time series forecasting and encourage to be applied for meteorological variables measurements such as soil moisture series, daily rainfall and monthly cumulative rainfall time series forecasting.
Fil: Rodriguez Rivero, Cristian Maximiliano. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina
Fil: Pucheta, Julián Antonio. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Baumgartner, Josef Sylvester. Universidad Nacional de Córdoba. Facultad de Cs.exactas Físicas y Naturales. Departamento de Electronica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Patiño, Héctor Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Sauchelli, Victor Hugo. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina
Materia
TIME SERIES
FORECASTING
NEURAL NETWORKS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/196019
Acceder
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network_name_str CONICET Digital (CONICET)
spelling High roughness time series forecasting based on energy associated of seriesRodriguez Rivero, Cristian MaximilianoPucheta, Julián AntonioBaumgartner, Josef SylvesterPatiño, Héctor DanielSauchelli, Victor HugoTIME SERIESFORECASTINGNEURAL NETWORKShttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2In this study, an algorithm to adjust parameters of high roughness time series based on energy associated of series using a feed-forward NN-based model is presented. The criterion for adjustment consists of building time series values from forecasted time series area and taking into account the roughness of series. These values are approximated by the NN to make a primitive calculated as an area by the predictor filter used as a new entrance. A comparison between this work and another that involves a similar approach to test time series prediction, indicates an improvement for certain sort of series. The NN filter output is intended to approximate the current value available from the series which has the same Hurst Parameter as the real time series. The proposed approach is tested over five time series obtained from samples of Mackey-Glass delay differential equations (MG). Therefore, these results show a model performance for time series forecasting and encourage to be applied for meteorological variables measurements such as soil moisture series, daily rainfall and monthly cumulative rainfall time series forecasting.Fil: Rodriguez Rivero, Cristian Maximiliano. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; ArgentinaFil: Pucheta, Julián Antonio. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Baumgartner, Josef Sylvester. Universidad Nacional de Córdoba. Facultad de Cs.exactas Físicas y Naturales. Departamento de Electronica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Patiño, Héctor Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Sauchelli, Victor Hugo. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; ArgentinaDavid Publishing Company2012-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/196019Rodriguez Rivero, Cristian Maximiliano; Pucheta, Julián Antonio; Baumgartner, Josef Sylvester; Patiño, Héctor Daniel; Sauchelli, Victor Hugo; High roughness time series forecasting based on energy associated of series; David Publishing Company; Journal of Communication and Computer; 5; 9; 5-2012; 576-5861548-7709CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.davidpublisher.com/index.php/Home/Journal/detail?journalid=16&jx=jcc&cont=allissuesinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:47:10Zoai:ri.conicet.gov.ar:11336/196019instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:47:11.484CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv High roughness time series forecasting based on energy associated of series
title High roughness time series forecasting based on energy associated of series
spellingShingle High roughness time series forecasting based on energy associated of series
Rodriguez Rivero, Cristian Maximiliano
TIME SERIES
FORECASTING
NEURAL NETWORKS
title_short High roughness time series forecasting based on energy associated of series
title_full High roughness time series forecasting based on energy associated of series
title_fullStr High roughness time series forecasting based on energy associated of series
title_full_unstemmed High roughness time series forecasting based on energy associated of series
title_sort High roughness time series forecasting based on energy associated of series
dc.creator.none.fl_str_mv Rodriguez Rivero, Cristian Maximiliano
Pucheta, Julián Antonio
Baumgartner, Josef Sylvester
Patiño, Héctor Daniel
Sauchelli, Victor Hugo
author Rodriguez Rivero, Cristian Maximiliano
author_facet Rodriguez Rivero, Cristian Maximiliano
Pucheta, Julián Antonio
Baumgartner, Josef Sylvester
Patiño, Héctor Daniel
Sauchelli, Victor Hugo
author_role author
author2 Pucheta, Julián Antonio
Baumgartner, Josef Sylvester
Patiño, Héctor Daniel
Sauchelli, Victor Hugo
author2_role author
author
author
author
dc.subject.none.fl_str_mv TIME SERIES
FORECASTING
NEURAL NETWORKS
topic TIME SERIES
FORECASTING
NEURAL NETWORKS
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv In this study, an algorithm to adjust parameters of high roughness time series based on energy associated of series using a feed-forward NN-based model is presented. The criterion for adjustment consists of building time series values from forecasted time series area and taking into account the roughness of series. These values are approximated by the NN to make a primitive calculated as an area by the predictor filter used as a new entrance. A comparison between this work and another that involves a similar approach to test time series prediction, indicates an improvement for certain sort of series. The NN filter output is intended to approximate the current value available from the series which has the same Hurst Parameter as the real time series. The proposed approach is tested over five time series obtained from samples of Mackey-Glass delay differential equations (MG). Therefore, these results show a model performance for time series forecasting and encourage to be applied for meteorological variables measurements such as soil moisture series, daily rainfall and monthly cumulative rainfall time series forecasting.
Fil: Rodriguez Rivero, Cristian Maximiliano. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina
Fil: Pucheta, Julián Antonio. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Baumgartner, Josef Sylvester. Universidad Nacional de Córdoba. Facultad de Cs.exactas Físicas y Naturales. Departamento de Electronica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Patiño, Héctor Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Sauchelli, Victor Hugo. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina
description In this study, an algorithm to adjust parameters of high roughness time series based on energy associated of series using a feed-forward NN-based model is presented. The criterion for adjustment consists of building time series values from forecasted time series area and taking into account the roughness of series. These values are approximated by the NN to make a primitive calculated as an area by the predictor filter used as a new entrance. A comparison between this work and another that involves a similar approach to test time series prediction, indicates an improvement for certain sort of series. The NN filter output is intended to approximate the current value available from the series which has the same Hurst Parameter as the real time series. The proposed approach is tested over five time series obtained from samples of Mackey-Glass delay differential equations (MG). Therefore, these results show a model performance for time series forecasting and encourage to be applied for meteorological variables measurements such as soil moisture series, daily rainfall and monthly cumulative rainfall time series forecasting.
publishDate 2012
dc.date.none.fl_str_mv 2012-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/196019
Rodriguez Rivero, Cristian Maximiliano; Pucheta, Julián Antonio; Baumgartner, Josef Sylvester; Patiño, Héctor Daniel; Sauchelli, Victor Hugo; High roughness time series forecasting based on energy associated of series; David Publishing Company; Journal of Communication and Computer; 5; 9; 5-2012; 576-586
1548-7709
CONICET Digital
CONICET
url http://hdl.handle.net/11336/196019
identifier_str_mv Rodriguez Rivero, Cristian Maximiliano; Pucheta, Julián Antonio; Baumgartner, Josef Sylvester; Patiño, Héctor Daniel; Sauchelli, Victor Hugo; High roughness time series forecasting based on energy associated of series; David Publishing Company; Journal of Communication and Computer; 5; 9; 5-2012; 576-586
1548-7709
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.davidpublisher.com/index.php/Home/Journal/detail?journalid=16&jx=jcc&cont=allissues
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv David Publishing Company
publisher.none.fl_str_mv David Publishing Company
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1842268841188524032
score 13.13397

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