In statistics, signal processing, and many other fields, a multivariate time series is a set of sequences of data points, measured typically at successive times, spaced at (often uniform) time intervals
Published in Chapter:
Clustering and Visualization of Multivariate Time Series
Alfredo Vellido (Universidad Politécnica de Cataluña, Spain) and Iván Olier (Universidad Politécnica de Cataluña, Spain)
Copyright: © 2010
|Pages: 19
DOI: 10.4018/978-1-60566-766-9.ch008
Abstract
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 through 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 applicability. Here, the authors reformulate it within a Bayesian approach using variational techniques. The resulting variational Bayesian GTM-TT, described in some detail, is shown to behave very robustly in the presence of noise in the MTS, helping to avert the problem of data overfitting.