  In many real-world application, e.g., speech recognition or sleep stage
classification, data are captured over the course of time, constituting a
Time-Series. Time-Series often contain temporal dependencies that cause two
otherwise identical points of time to belong to different classes or predict
different behavior. This characteristic generally increases the difficulty of
analysing them. Existing techniques often depended on hand-crafted features
that were expensive to create and required expert knowledge of the field. With
the advent of Deep Learning new models of unsupervised learning of features for
Time-series analysis and forecast have been developed. Such new developments
are the topic of this paper: a review of the main Deep Learning techniques is
presented, and some applications on Time-Series analysis are summaried. The
results make it clear that Deep Learning has a lot to contribute to the field.
