A regression model with a hidden logistic process for feature extraction from time series
Abstract
A new approach for feature extraction from time series is proposed in this paper. This approach consists of a specific regression model incorporating a discrete hidden logistic process. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. The parameters of the hidden logistic process, in the inner loop of the EM algorithm, are estimated using a multi-class Iterative Reweighted Least-Squares (IRLS) algorithm. A piecewise regression algorithm and its iterative variant have also been considered for comparisons. An experimental study using simulated and real data reveals good performances of the proposed approach.
Cite
@article{arxiv.1312.7001,
title = {A regression model with a hidden logistic process for feature extraction from time series},
author = {Faicel Chamroukhi and Allou Samé and Gérard Govaert and Patrice Aknin},
journal= {arXiv preprint arXiv:1312.7001},
year = {2013}
}
Comments
In Proceedings of the International Joint Conference on Neural Networks (IJCNN), 2009, Atlanta, USA