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On methods to assess the significance of community structure in networks of financial time series
dc.contributor | Arratia Quesada, Argimiro Alejandro |
dc.contributor.author | Renedo Mirambell, Martí |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.date.accessioned | 2017-07-20T07:31:52Z |
dc.date.available | 2017-07-20T07:31:52Z |
dc.date.issued | 2017-07 |
dc.identifier.uri | http://hdl.handle.net/2117/106628 |
dc.description.abstract | 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. |
dc.language.iso | eng |
dc.publisher | Universitat Politècnica de Catalunya |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica::Anàlisi multivariant |
dc.subject.lcsh | Multivariate analysis |
dc.subject.other | Clustering |
dc.subject.other | Financial time series |
dc.subject.other | Ground-truth communities |
dc.subject.other | Similarity measures |
dc.subject.other | Forex network |
dc.title | On methods to assess the significance of community structure in networks of financial time series |
dc.type | Master thesis |
dc.subject.lemac | Anàlisi multivariable |
dc.subject.ams | Classificació AMS::62 Statistics::62H Multivariate analysis |
dc.identifier.slug | FME-1518 |
dc.rights.access | Open Access |
dc.date.updated | 2017-07-14T08:13:17Z |
dc.audience.educationlevel | Màster |
dc.audience.mediator | Universitat Politècnica de Catalunya. Facultat de Matemàtiques i Estadística |
dc.audience.degree | MÀSTER UNIVERSITARI EN MATEMÀTICA AVANÇADA I ENGINYERIA MATEMÀTICA (Pla 2010) |
dc.contributor.covenantee | BGSMath |