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Daniel Pena, UC3M Print
Thursday, 30 November 2017, 12:15 - 13:15

Daniel Pena, Universidad Carlos III, Madrid

Clustering Time Series by Dependency

Abstract : A new way to find clusters in large vectors of time series is presented. We define a measure of similarity between two series, the generalized cross correlation, that takes into account all their cross correlations until some lag k. The measure is a ratio of the determinants of the correlation matrix of the bivariate vector and the two univariate time series. The matrix of similarities among the series based on this measure is used as input of a hierarchical clustering algorithm. The procedure is automatic and can be applied to large data sets. Clustering using this approach will be useful to build Dynamic Factor Models with Group Structure. The procedure is illustrated with some Monte Carlo experiments and a real data example.

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