  Time series classification is an important task in its own right, and it is
often a precursor to further downstream analytics. To date, virtually all works
in the literature have used either shape-based classification using a distance
measure or feature-based classification after finding some suitable features
for the domain. It seems to be underappreciated that in many datasets it is the
case that some classes are best discriminated with features, while others are
best discriminated with shape. Thus, making the shape vs. feature choice will
condemn us to poor results, at least for some classes. In this work, we propose
a new model for classifying time series that allows the use of both shape and
feature-based measures, when warranted. Our algorithm automatically decides
which approach is best for which class, and at query time chooses which
classifier to trust the most. We evaluate our idea on real world datasets and
demonstrate that our ideas produce statistically significant improvement in
classification accuracy.
