Symbolic Time Series and Causality Detection: an Uneasy Alliance

Autores
Contiggiani, Federico Eduardo; Delbianco, Fernando; Fioriti, Andrés; Tohmé, Fernando
Año de publicación
2021
Idioma
español castellano
Tipo de recurso
documento de conferencia
Estado
versión aceptada
Descripción
Fil: Contiggiani, Federico Eduardo. Universidad Nacional de Río Negro. Instituto de Investigación en Políticas Públicas y Gobierno. Río Negro; Argentina.
Fil: Delbianco, Fernando. Instituto de Matemática de Bahía Blanca, CONICET - Universidad Nacional del Sur. Buenos Aires. Argentina.
Fil: Fioriti, Andrés. Instituto de Matemática de Bahía Blanca, CONICET - Universidad Nacional del Sur. Buenos Aires. Argentina.
Fil: Tohmé, Fernando. Instituto de Matemática de Bahía Blanca, CONICET - Universidad Nacional del Sur. Buenos Aires. Argentina.
Symbolic Time Series Analysis (STSA) is a quantitative dominant mixed method applied in Economics and other Social Sciences as a way of reducing the impact of noise on data and to exhibit more clearly the evolution of time series. We show that such transformation from numerical to symbolic series may fail to preserve relevant properties of the original series. We focus, in particular, on the existence of causal relations among series. Well known methods of detection of causality, like Transfer Entropy or Granger’s Test can either yield non-existing causal relations or miss some actually existing ones, which is highly relevant for a sound application of a mixed method like STSA.
Materia
Economía y Contabilidad
Causality
Symbolic Time Series Analysis
Markov Switching Model
Transfer Entropy
Granger’s Test
Economía y Contabilidad
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
RID-UNRN (UNRN)
Institución
Universidad Nacional de Río Negro
OAI Identificador
oai:rid.unrn.edu.ar:20.500.12049/9551
Acceder
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network_name_str RID-UNRN (UNRN)
spelling Symbolic Time Series and Causality Detection: an Uneasy AllianceContiggiani, Federico EduardoDelbianco, FernandoFioriti, AndrésTohmé, FernandoEconomía y ContabilidadCausalitySymbolic Time Series AnalysisMarkov Switching ModelTransfer EntropyGranger’s TestEconomía y ContabilidadFil: Contiggiani, Federico Eduardo. Universidad Nacional de Río Negro. Instituto de Investigación en Políticas Públicas y Gobierno. Río Negro; Argentina.Fil: Delbianco, Fernando. Instituto de Matemática de Bahía Blanca, CONICET - Universidad Nacional del Sur. Buenos Aires. Argentina.Fil: Fioriti, Andrés. Instituto de Matemática de Bahía Blanca, CONICET - Universidad Nacional del Sur. Buenos Aires. Argentina.Fil: Tohmé, Fernando. Instituto de Matemática de Bahía Blanca, CONICET - Universidad Nacional del Sur. Buenos Aires. Argentina.Symbolic Time Series Analysis (STSA) is a quantitative dominant mixed method applied in Economics and other Social Sciences as a way of reducing the impact of noise on data and to exhibit more clearly the evolution of time series. We show that such transformation from numerical to symbolic series may fail to preserve relevant properties of the original series. We focus, in particular, on the existence of causal relations among series. Well known methods of detection of causality, like Transfer Entropy or Granger’s Test can either yield non-existing causal relations or miss some actually existing ones, which is highly relevant for a sound application of a mixed method like STSA.2021-10-26info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttps://www.youtube.com/watch?v=HbKt_B4kyAshttp://rid.unrn.edu.ar/handle/20.500.12049/9551spahttps://www.iiess-conicet.gov.ar/index.php/cursos-congresos-simposios-conferencias-workshops/la-actual-situacion-energetica-argentina-analisis-y-perspectivas/8-iiess/405-iv-jiscIV JISC - Jornadas Interdisciplinarias en Sistemas Complejos 2021info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/4.0/reponame:RID-UNRN (UNRN)instname:Universidad Nacional de Río Negro2025-10-23T11:17:33Zoai:rid.unrn.edu.ar:20.500.12049/9551instacron:UNRNInstitucionalhttps://rid.unrn.edu.ar/jspui/Universidad públicaNo correspondehttps://rid.unrn.edu.ar/oai/snrdrid@unrn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:43692025-10-23 11:17:34.166RID-UNRN (UNRN) - Universidad Nacional de Río Negrofalse
dc.title.none.fl_str_mv Symbolic Time Series and Causality Detection: an Uneasy Alliance
title Symbolic Time Series and Causality Detection: an Uneasy Alliance
spellingShingle Symbolic Time Series and Causality Detection: an Uneasy Alliance
Contiggiani, Federico Eduardo
Economía y Contabilidad
Causality
Symbolic Time Series Analysis
Markov Switching Model
Transfer Entropy
Granger’s Test
Economía y Contabilidad
title_short Symbolic Time Series and Causality Detection: an Uneasy Alliance
title_full Symbolic Time Series and Causality Detection: an Uneasy Alliance
title_fullStr Symbolic Time Series and Causality Detection: an Uneasy Alliance
title_full_unstemmed Symbolic Time Series and Causality Detection: an Uneasy Alliance
title_sort Symbolic Time Series and Causality Detection: an Uneasy Alliance
dc.creator.none.fl_str_mv Contiggiani, Federico Eduardo
Delbianco, Fernando
Fioriti, Andrés
Tohmé, Fernando
author Contiggiani, Federico Eduardo
author_facet Contiggiani, Federico Eduardo
Delbianco, Fernando
Fioriti, Andrés
Tohmé, Fernando
author_role author
author2 Delbianco, Fernando
Fioriti, Andrés
Tohmé, Fernando
author2_role author
author
author
dc.subject.none.fl_str_mv Economía y Contabilidad
Causality
Symbolic Time Series Analysis
Markov Switching Model
Transfer Entropy
Granger’s Test
Economía y Contabilidad
topic Economía y Contabilidad
Causality
Symbolic Time Series Analysis
Markov Switching Model
Transfer Entropy
Granger’s Test
Economía y Contabilidad
dc.description.none.fl_txt_mv Fil: Contiggiani, Federico Eduardo. Universidad Nacional de Río Negro. Instituto de Investigación en Políticas Públicas y Gobierno. Río Negro; Argentina.
Fil: Delbianco, Fernando. Instituto de Matemática de Bahía Blanca, CONICET - Universidad Nacional del Sur. Buenos Aires. Argentina.
Fil: Fioriti, Andrés. Instituto de Matemática de Bahía Blanca, CONICET - Universidad Nacional del Sur. Buenos Aires. Argentina.
Fil: Tohmé, Fernando. Instituto de Matemática de Bahía Blanca, CONICET - Universidad Nacional del Sur. Buenos Aires. Argentina.
Symbolic Time Series Analysis (STSA) is a quantitative dominant mixed method applied in Economics and other Social Sciences as a way of reducing the impact of noise on data and to exhibit more clearly the evolution of time series. We show that such transformation from numerical to symbolic series may fail to preserve relevant properties of the original series. We focus, in particular, on the existence of causal relations among series. Well known methods of detection of causality, like Transfer Entropy or Granger’s Test can either yield non-existing causal relations or miss some actually existing ones, which is highly relevant for a sound application of a mixed method like STSA.
description Fil: Contiggiani, Federico Eduardo. Universidad Nacional de Río Negro. Instituto de Investigación en Políticas Públicas y Gobierno. Río Negro; Argentina.
publishDate 2021
dc.date.none.fl_str_mv 2021-10-26
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/acceptedVersion
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://www.youtube.com/watch?v=HbKt_B4kyAs
http://rid.unrn.edu.ar/handle/20.500.12049/9551
url https://www.youtube.com/watch?v=HbKt_B4kyAs
http://rid.unrn.edu.ar/handle/20.500.12049/9551
dc.language.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv https://www.iiess-conicet.gov.ar/index.php/cursos-congresos-simposios-conferencias-workshops/la-actual-situacion-energetica-argentina-analisis-y-perspectivas/8-iiess/405-iv-jisc
IV JISC - Jornadas Interdisciplinarias en Sistemas Complejos 2021
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/4.0/
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instname:Universidad Nacional de Río Negro
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instname_str Universidad Nacional de Río Negro
repository.name.fl_str_mv RID-UNRN (UNRN) - Universidad Nacional de Río Negro
repository.mail.fl_str_mv rid@unrn.edu.ar
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score 12.982451

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