  Within the last years, the classification of variable stars with Machine
Learning has become a mainstream area of research. Recently, visualization of
time series is attracting more attention in data science as a tool to visually
help scientists to recognize significant patterns in complex dynamics. Within
the Machine Learning literature, dictionary-based methods have been widely used
to encode relevant parts of image data. These methods intrinsically assign a
degree of importance to patches in pictures, according to their contribution in
the image reconstruction. Inspired by dictionary-based techniques, we present
an approach that naturally provides the visualization of salient parts in
astronomical light curves, making the analogy between image patches and
relevant pieces in time series. Our approach encodes the most meaningful
patterns such that we can approximately reconstruct light curves by just using
the encoded information. We test our method in light curves from the OGLE-III
and StarLight databases. Our results show that the proposed model delivers an
automatic and intuitive visualization of relevant light curve parts, such as
local peaks and drops in magnitude.
