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

  • Iván Olier
  • Alfredo Vellido
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 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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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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