Capturing the Dynamics of Multivariate Time Series Through Visualization Using Generative Topographic Mapping Through Time

Most of the existing research on time series concerns supervised forecasting problems. In comparison, little research has been devoted to unsupervised methods for the visual exploration of multivariate time series. In this paper, the capabilities of the generative topographic mapping through time, a model with solid foundations in probability theory that performs simultaneous time series data clustering and visualization, are assessed in detail in several experiments. The focus is placed on the detection of atypical data, the visualization of the evolution of signal regimes, and the exploration of sudden transitions, for which a novel identification index is defined