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Found 10 papers, 1 papers with code
A Regression Mixture Model to understand the effect of the Covid-19 pandemic on Public Transport Ridership
To understand its effects on rail public transport ridership, we propose a dedicated Regression Mixture Model able to perform both the clustering of public transport stations and the segmentation of time periods, while ignoring variations due to additional variables such as the official lockdowns or non-working days.
Time series modeling by a regression approach based on a latent process
A new approach for time series modeling is proposed in this paper.
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.
Classification automatique de données temporelles en classes ordonnées
This paper proposes a method of segmenting temporal data into ordered classes.
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.
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.
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.
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 functional mixture discriminant analysis with hidden process regression for curve classification
We propose a new model-based functional mixture discriminant analysis approach based on a specific hidden process regression model that governs the regime changes over time.
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.
