On methods to assess the significance of community structure in networks of financial time series

We consider the problem of determining whether the community structure found by a clustering algorithm applied to financial time series is statistically significant, when no other information than the observed values and a similarity measure among time series is available. We propose two raw-data ba...

Descripción completa

Detalles Bibliográficos
Autor: Renedo Mirambell, Martí
Tipo de recurso: tesis de maestría
Fecha de publicación:2017
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/106628
Acceso en línea:https://hdl.handle.net/2117/106628
Access Level:acceso abierto
Palabra clave:Multivariate analysis
Clustering
Financial time series
Ground-truth communities
Similarity measures
Forex network
Anàlisi multivariable
Classificació AMS::62 Statistics::62H Multivariate analysis
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica::Anàlisi multivariant
id ES_dd10027a741db44e2f85437b92a09cc4
oai_identifier_str oai:upcommons.upc.edu:2117/106628
network_acronym_str ES
network_name_str España
repository_id_str
spelling On methods to assess the significance of community structure in networks of financial time seriesRenedo Mirambell, MartíMultivariate analysisClusteringFinancial time seriesGround-truth communitiesSimilarity measuresForex networkAnàlisi multivariableClassificació AMS::62 Statistics::62H Multivariate analysisÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica::Anàlisi multivariantWe consider the problem of determining whether the community structure found by a clustering algorithm applied to financial time series is statistically significant, when no other information than the observed values and a similarity measure among time series is available. We propose two raw-data based methods for assessing robustness of clustering algorithms on time-dependent data linked by a relation of similarity: One based on community scoring functions that quantify some topological property that characterizes ground-truth communities, the other based on random perturbations and quantification of the variation in the community structure. These methodologies are well-established in the realm of unweighted networks; our contribution are versions adapted to complete weighted networks. We reinforce our assessment of the accuracy of the clustering algorithm by testing its performance on synthetic ground-truth communities of time series built through Monte Carlo simulations of VARMA processes.Universitat Politècnica de CatalunyaArratia Quesada, Argimiro Alejandro20172017-07-0120172017-07-20master thesishttp://purl.org/coar/resource_type/c_bdccNAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/2117/106628reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2http://creativecommons.org/licenses/by-nc-sa/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/1066282026-05-27T15:37:01Z
dc.title.none.fl_str_mv On methods to assess the significance of community structure in networks of financial time series
title On methods to assess the significance of community structure in networks of financial time series
spellingShingle On methods to assess the significance of community structure in networks of financial time series
Renedo Mirambell, Martí
Multivariate analysis
Clustering
Financial time series
Ground-truth communities
Similarity measures
Forex network
Anàlisi multivariable
Classificació AMS::62 Statistics::62H Multivariate analysis
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica::Anàlisi multivariant
title_short On methods to assess the significance of community structure in networks of financial time series
title_full On methods to assess the significance of community structure in networks of financial time series
title_fullStr On methods to assess the significance of community structure in networks of financial time series
title_full_unstemmed On methods to assess the significance of community structure in networks of financial time series
title_sort On methods to assess the significance of community structure in networks of financial time series
dc.creator.none.fl_str_mv Renedo Mirambell, Martí
author Renedo Mirambell, Martí
author_facet Renedo Mirambell, Martí
author_role author
dc.contributor.none.fl_str_mv Arratia Quesada, Argimiro Alejandro
dc.subject.none.fl_str_mv Multivariate analysis
Clustering
Financial time series
Ground-truth communities
Similarity measures
Forex network
Anàlisi multivariable
Classificació AMS::62 Statistics::62H Multivariate analysis
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica::Anàlisi multivariant
topic Multivariate analysis
Clustering
Financial time series
Ground-truth communities
Similarity measures
Forex network
Anàlisi multivariable
Classificació AMS::62 Statistics::62H Multivariate analysis
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica::Anàlisi multivariant
description We consider the problem of determining whether the community structure found by a clustering algorithm applied to financial time series is statistically significant, when no other information than the observed values and a similarity measure among time series is available. We propose two raw-data based methods for assessing robustness of clustering algorithms on time-dependent data linked by a relation of similarity: One based on community scoring functions that quantify some topological property that characterizes ground-truth communities, the other based on random perturbations and quantification of the variation in the community structure. These methodologies are well-established in the realm of unweighted networks; our contribution are versions adapted to complete weighted networks. We reinforce our assessment of the accuracy of the clustering algorithm by testing its performance on synthetic ground-truth communities of time series built through Monte Carlo simulations of VARMA processes.
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-07-01
2017
2017-07-20
dc.type.none.fl_str_mv master thesis
http://purl.org/coar/resource_type/c_bdcc
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/106628
url https://hdl.handle.net/2117/106628
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-sa/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-sa/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universitat Politècnica de Catalunya
publisher.none.fl_str_mv Universitat Politècnica de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869421825750990848
score 15,300719

Similares en LA Referencia