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Título:
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On methods to assess the significance of community structure in networks of financial time series
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Autor/a:
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Renedo Mirambell, Martí
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Otros autores:
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Universitat Politècnica de Catalunya. Departament de Ciències de la Computació; BGSMath; Arratia Quesada, Argimiro Alejandro |
Abstract:
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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. |
Materia(s):
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-Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica::Anàlisi multivariant -Multivariate analysis -Clustering -Financial time series -Ground-truth communities -Similarity measures -Forex network -Anàlisi multivariable -Classificació AMS::62 Statistics::62H Multivariate analysis |
Derechos:
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http://creativecommons.org/licenses/by-nc-sa/3.0/es/ |
Tipo de documento:
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Trabajo fin de máster |
Editor:
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Universitat Politècnica de Catalunya
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