Clustering time series by linear dependency. (English) Zbl 1430.62191

Summary: We present a new way to find clusters in large vectors of time series by using a measure of similarity between two time series, the generalized cross correlation. This measure compares the determinant of the correlation matrix until some lag \(k\) of the bivariate vector with those of the two univariate time series. A matrix of similarities among the series based on this measure is used as input of a clustering algorithm. The procedure is automatic, can be applied to large data sets and it is useful to find groups in dynamic factor models. The cluster method is illustrated with some Monte Carlo experiments and a real data example.


62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
62H30 Classification and discrimination; cluster analysis (statistical aspects)
65C05 Monte Carlo methods


Silhouettes; Tsclust
Full Text: DOI


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