Extracting Driving Signals from Non-Stationary Time Series

Autores
Széliga, M. I. S; Verdes, Pablo Fabián; Granitto, Pablo Miguel; Ceccatto, Hermenegildo Alejandro
Año de publicación
2002
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
We propose a simple method for the reconstruction of slow dynamics perturbations from non-stationary time series records. The method traces the evolution of the perturbing signal by simultaneously learning the intrinsic stationary dynamics and the time dependency of the changing parameter. For this purpose, an extra input unit is added to a feedforward artificial neural network and a suitable error function minimized in the training process. Testing of our algorithm on synthetic data shows its efficacy and allows extracting general criteria for applications on real-world problems. Finally, a preliminary study of the well-known sunspot time series recovers particular features of this series, including recently reported changes in solar activity during last century.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
non-stationary time series
perturbing signal
simultaneously learning
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/183226
Acceder
id SEDICI_73099bde7e63ca86077ab7ae83462438
oai_identifier_str oai:sedici.unlp.edu.ar:10915/183226
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Extracting Driving Signals from Non-Stationary Time SeriesSzéliga, M. I. SVerdes, Pablo FabiánGranitto, Pablo MiguelCeccatto, Hermenegildo AlejandroCiencias Informáticasnon-stationary time seriesperturbing signalsimultaneously learningWe propose a simple method for the reconstruction of slow dynamics perturbations from non-stationary time series records. The method traces the evolution of the perturbing signal by simultaneously learning the intrinsic stationary dynamics and the time dependency of the changing parameter. For this purpose, an extra input unit is added to a feedforward artificial neural network and a suitable error function minimized in the training process. Testing of our algorithm on synthetic data shows its efficacy and allows extracting general criteria for applications on real-world problems. Finally, a preliminary study of the well-known sunspot time series recovers particular features of this series, including recently reported changes in solar activity during last century.Sociedad Argentina de Informática e Investigación Operativa2002info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf262-271http://sedici.unlp.edu.ar/handle/10915/183226enginfo:eu-repo/semantics/altIdentifier/issn/1660-1079info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-15T11:41:53Zoai:sedici.unlp.edu.ar:10915/183226Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 11:41:54.204SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Extracting Driving Signals from Non-Stationary Time Series
title Extracting Driving Signals from Non-Stationary Time Series
spellingShingle Extracting Driving Signals from Non-Stationary Time Series
Széliga, M. I. S
Ciencias Informáticas
non-stationary time series
perturbing signal
simultaneously learning
title_short Extracting Driving Signals from Non-Stationary Time Series
title_full Extracting Driving Signals from Non-Stationary Time Series
title_fullStr Extracting Driving Signals from Non-Stationary Time Series
title_full_unstemmed Extracting Driving Signals from Non-Stationary Time Series
title_sort Extracting Driving Signals from Non-Stationary Time Series
dc.creator.none.fl_str_mv Széliga, M. I. S
Verdes, Pablo Fabián
Granitto, Pablo Miguel
Ceccatto, Hermenegildo Alejandro
author Széliga, M. I. S
author_facet Széliga, M. I. S
Verdes, Pablo Fabián
Granitto, Pablo Miguel
Ceccatto, Hermenegildo Alejandro
author_role author
author2 Verdes, Pablo Fabián
Granitto, Pablo Miguel
Ceccatto, Hermenegildo Alejandro
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
non-stationary time series
perturbing signal
simultaneously learning
topic Ciencias Informáticas
non-stationary time series
perturbing signal
simultaneously learning
dc.description.none.fl_txt_mv We propose a simple method for the reconstruction of slow dynamics perturbations from non-stationary time series records. The method traces the evolution of the perturbing signal by simultaneously learning the intrinsic stationary dynamics and the time dependency of the changing parameter. For this purpose, an extra input unit is added to a feedforward artificial neural network and a suitable error function minimized in the training process. Testing of our algorithm on synthetic data shows its efficacy and allows extracting general criteria for applications on real-world problems. Finally, a preliminary study of the well-known sunspot time series recovers particular features of this series, including recently reported changes in solar activity during last century.
Sociedad Argentina de Informática e Investigación Operativa
description We propose a simple method for the reconstruction of slow dynamics perturbations from non-stationary time series records. The method traces the evolution of the perturbing signal by simultaneously learning the intrinsic stationary dynamics and the time dependency of the changing parameter. For this purpose, an extra input unit is added to a feedforward artificial neural network and a suitable error function minimized in the training process. Testing of our algorithm on synthetic data shows its efficacy and allows extracting general criteria for applications on real-world problems. Finally, a preliminary study of the well-known sunspot time series recovers particular features of this series, including recently reported changes in solar activity during last century.
publishDate 2002
dc.date.none.fl_str_mv 2002
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/183226
url http://sedici.unlp.edu.ar/handle/10915/183226
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/1660-1079
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
262-271
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
_version_ 1846064427184422912
score 13.22299

Publicaciones similares