samurais-package: SaMUraiS: StAtistical Models for the UnsupeRvised...

Description Author(s) References See Also


samurais is a toolbox including many original and flexible user-friendly statistical latent variable models and efficient unsupervised algorithms to segment and represent time-series data (univariate or multivariate), and more generally, longitudinal data, which include regime changes.

samurais contains the following time series segmentation models:

For the advantages/differences of each of them, the user is referred to our mentioned paper references.

To learn more about samurais, start with the vignettes: browseVignettes(package = "samurais")


Maintainer: Florian Lecocq



Chamroukhi, F., and Hien D. Nguyen. 2019. Model-Based Clustering and Classification of Functional Data. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery.

Chamroukhi, F. 2015. Statistical Learning of Latent Data Models for Complex Data Analysis. Habilitation Thesis (HDR), Universite de Toulon.

Trabelsi, D., S. Mohammed, F. Chamroukhi, L. Oukhellou, and Y. Amirat. 2013. An Unsupervised Approach for Automatic Activity Recognition Based on Hidden Markov Model Regression. IEEE Transactions on Automation Science and Engineering 3 (10): 829–335.

Chamroukhi, F., D. Trabelsi, S. Mohammed, L. Oukhellou, and Y. Amirat. 2013. Joint Segmentation of Multivariate Time Series with Hidden Process Regression for Human Activity Recognition. Neurocomputing 120: 633–44.

Chamroukhi, F., A. Same, G. Govaert, and P. Aknin. 2010. A Hidden Process Regression Model for Functional Data Description. Application to Curve Discrimination. Neurocomputing 73 (7-9): 1210–21.

Chamroukhi, F. 2010. Hidden Process Regression for Curve Modeling, Classification and Tracking. Ph.D. Thesis, Universite de Technologie de Compiegne.

Chamroukhi, F., A. Same, G. Govaert, and P. Aknin. 2009. Time Series Modeling by a Regression Approach Based on a Latent Process. Neural Networks 22 (5-6): 593–602.

See Also

Useful links:

samurais documentation built on July 28, 2019, 5:02 p.m.