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In this paper we consider functional data with heterogeneity in time and inpopulation. We propose a mixture model with segmentation of time to representthis heterogeneity while keeping the functional structure. Maximum likelihoodestimator is considered, proved to be identifiable and consistent. In practice,an EM algorithm is used, combined with dynamic programming for the maximizationstep, to approximate the maximum likelihood estimator. The method isillustrated on a simulated dataset, and used on a real dataset of electricityconsumption.

Mixture of segmentation for heterogeneous functional data