Hidden Markov Model Regression (HMMR) for Times Series Segmentation
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MAP.m
README.md
designmatrix.m
forwards_backwards.m
hmm_process.m
init_hmmr.m
learn_hmmr.m
main_HMMR.m
mk_stochastic.m
normalise.m
real_time_series_1.mat
real_time_series_2.mat
sample_hmmr.m
show_HMMR_results.m
simulated_time_series.mat

README.md

HMMR

User-freindly and flexible model anf algorithm for time series segmentation with a Regression model with a Hidden Markov Model Regression (HMMR).

Hidden Markov Model Regression (HMMR) for segmentation of time series with regime changes. The model assumes that the time series is governed by a sequence of hidden discrete regimes/states, where each regime/state has Gaussian regressors as observations. The model parameters are estimated by MLE via the EM algorithm

Faicel Chamroukhi

Please cite the following papers for this code:

@article{Chamroukhi-FDA-2018,
	Journal = {Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},
	Author = {Faicel Chamroukhi and Hien D. Nguyen},
	Note = {DOI: 10.1002/widm.1298.},
	Volume = {},
	Title = {Model-Based Clustering and Classification of Functional Data},
	Year = {2019},
	Month = {to appear},
	url =  {https://chamroukhi.com/papers/MBCC-FDA.pdf}
	}

@InProceedings{Chamroukhi-IJCNN-2011,
  author = {F. Chamroukhi and A. Sam\'e  and P. Aknin and G. Govaert},
  title = {Model-based clustering with Hidden Markov Model regression for time series with regime changes},
  Booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN), IEEE},
  Pages = {2814--2821},
  Adress = {San Jose, California, USA},
  year = {2011},
  month = {Jul-Aug},
  url = {https://chamroukhi.com/papers/Chamroukhi-ijcnn-2011.pdf}
}

@INPROCEEDINGS{Chamroukhi-IJCNN-2009,
  AUTHOR =       {Chamroukhi, F. and Sam\'e,  A. and Govaert, G. and Aknin, P.},
  TITLE =        {A regression model with a hidden logistic process for feature extraction from time series},
  BOOKTITLE =    {International Joint Conference on Neural Networks (IJCNN)},
  YEAR =         {2009},
  month = {June},
  pages = {489--496},
  Address = {Atlanta, GA},
 url = {https://chamroukhi.com/papers/chamroukhi_ijcnn2009.pdf}
}

@article{chamroukhi_et_al_NN2009,
	Address = {Oxford, UK, UK},
	Author = {Chamroukhi, F. and Sam\'{e}, A. and Govaert, G. and Aknin, P.},
	Date-Added = {2014-10-22 20:08:41 +0000},
	Date-Modified = {2014-10-22 20:08:41 +0000},
	Journal = {Neural Networks},
	Number = {5-6},
	Pages = {593--602},
	Publisher = {Elsevier Science Ltd.},
	Title = {Time series modeling by a regression approach based on a latent process},
	Volume = {22},
	Year = {2009},
	url  = {https://chamroukhi.com/papers/Chamroukhi_Neural_Networks_2009.pdf}
	}

Faicel Chamroukhi Septembre 2008.