Skip to main content

Research Repository

Advanced Search

Advances in clustering and visualization of time series using GTM through time

Abstract

Most of the existing research on multivariate time series concerns supervised forecasting problems. In comparison, little research has been devoted to their exploration through unsupervised clustering and visualization. In this paper, the capabilities of Generative Topographic Mapping Through Time, a model with foundations in probability theory, that performs simultaneous time series clustering and visualization, are assessed in detail. Focus is placed on the visualization of the evolution of signal regimes and the exploration of sudden transitions, for which a novel identification index is defined. The interpretability of time series clustering results may become extremely difficult, even in exploratory visualization, for high dimensional datasets. Here, we define and test an unsupervised time series relevance determination method, fully integrated in the Generative Topographic Mapping Through Time model, that can be used as a basis for time series selection. This method should ease the interpretation of time series clustering results.

Citation

(2008). Advances in clustering and visualization of time series using GTM through time. https://doi.org/10.1016/j.neunet.2008.05.013

Acceptance Date May 9, 2008
Publication Date Sep 1, 2008
Journal Neural Networks
Print ISSN 1879-2782
Pages 904 -913
DOI https://doi.org/10.1016/j.neunet.2008.05.013
Keywords multivariate time series, generative topographic mapping, unsupervised relevance determination, clustering, visualization, change point detection
Publisher URL http://dx.doi.org/10.1016/j.neunet.2008.05.013


Downloadable Citations