  Manifold learning techniques for dynamical systems and time series have shown
their utility for a broad spectrum of applications in recent years. While these
methods are effective at learning a low-dimensional representation, they are
often insufficient for visualizing the global and local structure of the data.
In this paper, we present DIG (Dynamical Information Geometry), a visualization
method for multivariate time series data that extracts an information geometry
from a diffusion framework. Specifically, we implement a novel group of
distances in the context of diffusion operators, which may be useful to reveal
structure in the data that may not be accessible by the commonly used diffusion
distances. Finally, we present a case study applying our visualization tool to
EEG data to visualize sleep stages.
