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Found 8 papers, 0 papers with code
Time series modeling by a regression approach based on a latent process
A new approach for time series modeling is proposed in this paper.
Classification automatique de données temporelles en classes ordonnées
This paper proposes a method of segmenting temporal data into ordered classes.
A regression model with a hidden logistic process for feature extraction from time series
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 hidden process regression model for functional data description. Application to curve discrimination
A new approach for functional data description is proposed in this paper.
Model-based clustering with Hidden Markov Model regression for time series with regime changes
Comparisons with existing approaches for time series clustering, including the stand EM for Gaussian mixtures, $K$-means clustering, the standard mixture of regression models and mixture of Hidden Markov Models, demonstrate the effectiveness of the proposed approach.
A regression model with a hidden logistic process for signal parametrization
A new approach for signal parametrization, which consists of a specific regression model incorporating a discrete hidden logistic process, is proposed.
Model-based clustering and segmentation of time series with changes in regime
The proposed approach can also be regarded as a clustering approach which operates by finding groups of time series having common changes in regime.
Modèle à processus latent et algorithme EM pour la régression non linéaire
A non linear regression approach which consists of a specific regression model incorporating a latent process, allowing various polynomial regression models to be activated preferentially and smoothly, is introduced in this paper.
