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  Model-based clustering with Hidden Markov Models and its application to financial times-series data

Knab, B., Schliep, A., Steckemetz, B., & Wichern, B. (2003). Model-based clustering with Hidden Markov Models and its application to financial times-series data. /: /.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-8B21-B Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-8B22-9
Genre: Proceedings

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Schliep.pdf (Any fulltext), 118KB
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 Creators:
Knab, B., Author
Schliep, A.1, Author              
Steckemetz, B., Author
Wichern, B., Author
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1Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433547              

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 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 non-Markovian nature of some aspects of the data we also extend classical Hidden Markov Models to employ 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, a proof of convergence for the training procedure and an evaluation of the method on simulated time-series data as well as on large data sets of financial time-series from the Public Saving and Loan Banks in Germany.

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Language(s): eng - English
 Dates: 2003
 Publication Status: Published in print
 Pages: /
 Publishing info: / : /
 Table of Contents: -
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 Identifiers: eDoc: 175868
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Title: GfKI 2002 - 26th Annual Conference of the Gesellschaft für Klassifikation
Place of Event: Mannheim
Start-/End Date: 2002-07-22 - 2002-07-24

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