Advances in Data Analysis and Classification

, Volume 5, Issue 4, pp 301–321 | Cite as

Model-based clustering and segmentation of time series with changes in regime

  • Allou Samé
  • Faicel Chamroukhi
  • Gérard Govaert
  • Patrice Aknin
Regular Article


Mixture model-based clustering, usually applied to multidimensional data, has become a popular approach in many data analysis problems, both for its good statistical properties and for the simplicity of implementation of the Expectation–Maximization (EM) algorithm. Within the context of a railway application, this paper introduces a novel mixture model for dealing with time series that are subject to changes in regime. The proposed approach, called ClustSeg, consists in modeling each cluster by a regression model in which the polynomial coefficients vary according to a discrete hidden process. In particular, this approach makes use of logistic functions to model the (smooth or abrupt) transitions between regimes. The model parameters are estimated by the maximum likelihood method solved by an EM algorithm. This approach can also be regarded as a clustering approach which operates by finding groups of time series having common changes in regime. In addition to providing a time series partition, it therefore provides a time series segmentation. The problem of selecting the optimal numbers of clusters and segments is solved by means of the Bayesian Information Criterion. The ClustSeg approach is shown to be efficient using a variety of simulated time series and real-world time series of electrical power consumption from rail switching operations.


Clustering Time series Change in regime Mixture model Regression mixture Hidden logistic process EM algorithm 

Mathematics Subject Classification (2010)

62-07 62M10 62H30 


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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Allou Samé
    • 1
  • Faicel Chamroukhi
    • 1
  • Gérard Govaert
    • 2
  • Patrice Aknin
    • 1
  1. 1.Université Paris-Est, IFSTTAR, GRETTIANoisy-le-GrandFrance
  2. 2.Université de Technologie de Compiègne, UMR CNRS 6599CompiègneFrance

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