Mixture of segmentation for heterogeneous functional data
Methodology
2024-07-24 v3 Applications
Computation
Machine Learning
Abstract
In this paper we consider functional data with heterogeneity in time and in population. We propose a mixture model with segmentation of time to represent this heterogeneity while keeping the functional structure. Maximum likelihood estimator is considered, proved to be identifiable and consistent. In practice, an EM algorithm is used, combined with dynamic programming for the maximization step, to approximate the maximum likelihood estimator. The method is illustrated on a simulated dataset, and used on a real dataset of electricity consumption.
Cite
@article{arxiv.2303.10712,
title = {Mixture of segmentation for heterogeneous functional data},
author = {Vincent Brault and Émilie Devijver and Charlotte Laclau},
journal= {arXiv preprint arXiv:2303.10712},
year = {2024}
}
Comments
45 pages, 13 figures