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Model-Based Clustering With Hidden Markov Models and its Application to Financial Time-Series Data

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Between Data Science and Applied Data Analysis

Abstract

We have developed a method to partition a set of data into clusters by use of Hidden Markov Models. Given a number of clusters, each of which is represented by one Hidden Markov Model, an iterative procedure finds the combination of cluster models and an assignment of data points to cluster models which maximizes the joint likelihood of the clustering. To reflect the partially non-Markovian nature of the data we also extend classical Hidden Markov Models to use a non-homogeneous Markov chain, where the non-homogeneity is dependent not on the time of the observation but rather on a quantity derived from previous observations.

We present the method and an evaluation on simulated time-series and large data sets of financial time-series from the Public Saving and Loan Banks in Germany.

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Author information

Authors and Affiliations

  1. Bayer AG, D-51368, Leverkusen, Germany

    Bernhard Knab

  2. Department Computational Molecular Biology, Max-Planck-Institut für Molekulare Genetik, D-14195, Berlin, Germany

    Alexander Schliep

  3. Science Factory GmbH, D-50667, Köln, Germany

    Barthel Steckemetz

  4. ifb AG, D-50667, Köln, Germany

    Bernd Wichern

Authors
  1. Bernhard Knab
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  2. Alexander Schliep
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  3. Barthel Steckemetz
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  4. Bernd Wichern
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Editor information

Editors and Affiliations

  1. Department of Information Systems, University of Mannheim, Schloss, 68131, Mannheim, Germany

    Martin Schader

  2. Institute of Decision Theory, University of Karlsruhe, Kaiserstr. 12, 76128, Karlsruhe, Germany

    Wolfgang Gaul

  3. Department of Statistics, University of Rome, Piazzale Aldo Moro, 00185, Rome, Italy

    Maurizio Vichi

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© 2003 Springer-Verlag Berlin Heidelberg

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Knab, B., Schliep, A., Steckemetz, B., Wichern, B. (2003). Model-Based Clustering With Hidden Markov Models and its Application to Financial Time-Series Data. In: Schader, M., Gaul, W., Vichi, M. (eds) Between Data Science and Applied Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18991-3_64

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  • DOI: https://doi.org/10.1007/978-3-642-18991-3_64

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40354-8

  • Online ISBN: 978-3-642-18991-3

  • eBook Packages: Springer Book Archive

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