Research on automatic grouping of multivariate time series based on the information granularity

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In this research we have developed a multiscale comparison method for multivariate time series. Our method firstly constructs multidimensional trajectories from the time series, and represent them using multiscale representation. Next, it splits the trajectories into data granules according to the positions of curvature maxima. Then it traces the hierarchical structure of data granules and performs granule-by-granule matching across the scales to find the best correspondences between the trajectories. Experimental results on a medical dataset showed that our method could generate groups of trajectories that exhibited similar temporal courses, and some of the clusters showed interesting characteristics about the distribution of fibrotic stages.