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
- Institución
- Universidad Nacional de Río Negro
- OAI Identificador
- oai:rid.unrn.edu.ar:20.500.12049/9551
Ver los metadatos del registro completo
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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 Negro2025-09-29T14:29:14Zoai: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:43692025-09-29 14:29:14.939RID-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 |
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https://www.youtube.com/watch?v=HbKt_B4kyAs http://rid.unrn.edu.ar/handle/20.500.12049/9551 |
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https://www.youtube.com/watch?v=HbKt_B4kyAs http://rid.unrn.edu.ar/handle/20.500.12049/9551 |
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spa |
language |
spa |
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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 |
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