Clustering time series by linear dependency

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.

correlation coefficient; correlation matrix; dynamic factor models; unsupervised learning

Clustering time series by linear dependency Articles