Discrimination and classification time series becomes almost indispensable since the large amountof information available nowadays. The problem of time series discrimination and classification isdiscussed in [1]. In this work the authors propose a novel clustering algorithm based on a class ofquasi U-statistics and subgroup decomposition tests. The decomposition may be applied to any con-cave time-series distance. The resulting test statistics is proved to be asymptotically normal for eitheri.i.d. or non-identically distributed groups of time-series under mild conditions. In practice thereare many time series that are correlated among themselves. An example that can describe this fact isthe financial markets globalization. When one of these markets is affected by an exogenous factor, achain reaction can affect many others. So the independence condition fail.We are interested in analyzing how the correlation among the groups of time series can affectclassification and clustering methods especially the one proposed by [1]. Empirical results show thatthe proposed method is robust to the presence of correlation among time series. The convergence ofthe test statistic for dependent time series will be one of the goals in this work.

References[1]

Valk, M., Pinheiro, A. 2012: Time-series clustering via quasi U-statistics,J. Time Ser. Anal.,Vol. 33, 4, 608 -619.

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