Time Series Relevance Determination Through a Topology-Constrained Hidden Markov Model

  • Iván Olier
  • Alfredo Vellido
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)


Most of the existing research on multivariate time series concerns supervised forecasting problems. In comparison, little research has been devoted to unsupervised methods for the visual exploration of this type of data. The interpretability of time series clustering results may be difficult, even in exploratory visualization, for high dimensional datasets. In this paper, we define and test an unsupervised time series relevance determination method for Generative Topographic Mapping Through Time, a topology-constrained Hidden Markov Model that performs simultaneous time series data clustering and visualization. This relevance determination method can be used as a basis for time series selection, and should ease the interpretation of the time series clustering results.


Time Series Feature Selection Latent Variable Model Multivariate Time Series Unsupervised Method 


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  1. 1.
    Zhang, G., Patuwo, B., Hu, M.: Forecasting with artificial neural networks: The state of the art. Int. J. of Forecasting 14, 35–62 (1998)CrossRefGoogle Scholar
  2. 2.
    Chappel, G., Taylor, J.: The temporal Kohonen map. Neural Networks 6, 441–445 (1993)CrossRefGoogle Scholar
  3. 3.
    Strickert, M., Hammer, B.: Merge SOM for temporal data. Neurocomputing 64, 39–71 (2005)CrossRefGoogle Scholar
  4. 4.
    Voegtlin, T.: Recursive self-organizing maps. Neural Networks 15, 979–991 (2002)CrossRefGoogle Scholar
  5. 5.
    Bishop, C., Svensen, M., Williams, C.: GTM: The Generative Topographic Mapping. Neural Comput. 10, 215–234 (1998)CrossRefGoogle Scholar
  6. 6.
    Bishop, C., Hinton, G., Strachan, I.: GTM through time. In: IEEE Fifth Int. Conf. on Artif. Neural Net., Cambridge, U.K., pp. 111–116 (1997)Google Scholar
  7. 7.
    Olier, I., Vellido, A.: Capturing the dynamics of multivariate time series through visualization using Generative Topographic Mapping Through Time. In: IEEE ICEIS 2006, Islamabad, Pakistan (2006)Google Scholar
  8. 8.
    Yoon, H., Yang, K., Shahabi, C.: Feature subset selection and feature ranking for multivariate time series. IEEE Trans. on Knowledge and Data Eng. 17, 1186–1198 (2005)CrossRefGoogle Scholar
  9. 9.
    Vellido, A., Lisboa, P.J.G., Vicente, D.: Robust analysis of MRS brain tumour data using t-GTM. Neurocomputing Accepted for publication (in press)Google Scholar
  10. 10.
    Baum, L., Egon, J.: An inequality with applications to statistical estimation for probabilistic functions for a Markov process and to a model for ecology. B. Am. Meteorol. Soc. 73, 360–363 (1967)MATHGoogle Scholar
  11. 11.
    Law, M.H.C., Figueredo, M.A.T., Jain, A.K.: Simultaneous feature selection and clustering using mixture models. IEEE Trans. Pattern Anal. 26, 1154–1166 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Iván Olier
    • 1
  • Alfredo Vellido
    • 1
  1. 1.Department of Computing Languages and Systems (LSI)Polytechnic University of Catalonia (UPC)BarcelonaSpain

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