On methods to assess the significance of community structure in networks of financial time series
Document typeConference lecture
Rights accessOpen Access
We consider the problem of determining whether the community structure found by a clustering algorithm applied to nancial time series is statistically signi cant, or is due to pure chance, when no other information than the observed values and a similarity measure among time series are available. As a subsidiary problem we also analyse the in uence of the choice of similarity measure in the accuracy of the clustering method. 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 characterises ground-truth communities, and another based on random perturbations and quanti cation of the variation in the community structure. These methodologies are well-established in the realm of unweighted networks; our contribution are versions of these methodologies properly adapted to complete weighted networks.
CitationArratia, A.; Renedo, M. On methods to assess the significance of community structure in networks of financial time series. A: International Work-conference on Time Series. "Proceedings ITISE 2017. International work-conference on Time Series". 2017, p. 585-596.