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

In samurais: Statistical Models for the Unsupervised Segmentation of Time-Series ('SaMUraiS')

Description

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:

• RHLP;

• HMM/HMMR;

• PWR;

• MRHLP;

• MHMMR;

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")

Author(s)

Maintainer: Florian Lecocq [e-mail]

Authors:

• Faicel Chamroukhi [e-mail] (0000-0002-5894-3103)

• Marius Bartcus [e-mail]

References

Chamroukhi, F., and Hien D. Nguyen. 2019. Model-Based Clustering and Classification of Functional Data. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. https://doi.org/10.1002/widm.1298.

Chamroukhi, F. 2015. Statistical Learning of Latent Data Models for Complex Data Analysis. Habilitation Thesis (HDR), Universite de Toulon. https://chamroukhi.com/Dossier/FChamroukhi-Habilitation.pdf.

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. https://chamroukhi.com/papers/Chamroukhi-MHMMR-IeeeTase.pdf.

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. https://chamroukhi.com/papers/chamroukhi_et_al_neucomp2013b.pdf.

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. https://chamroukhi.com/papers/chamroukhi_neucomp_2010.pdf.

Chamroukhi, F. 2010. Hidden Process Regression for Curve Modeling, Classification and Tracking. Ph.D. Thesis, Universite de Technologie de Compiegne. https://chamroukhi.com/papers/FChamroukhi-Thesis.pdf.

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. https://chamroukhi.com/papers/Chamroukhi_Neural_Networks_2009.pdf.

See Also

Useful links:

• https://github.com/fchamroukhi/SaMUraiS

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