The exploratory investigation of multivariate time series (MTS) may become extremely difficult, if not impossible, for high dimensional datasets. Paradoxically, to date, little research has been conducted on the exploration of MTS trough unsupervised clustering and visualization. In this chapter, the authors describe generative topographic mapping through time (GTM-TT), a model with foundations in probability theory that performs such tasks. The standard version of this model has several limitations that limit its applicablility. Here, the authors reformulate it within a Bayesian approach using variational techniques. The resulting variational Bayesian GTM-TT, described in some details, is shown to behave very robustly in the presence of noise in the MTS, helping to avert the poblem of data overfitting.

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