  The time series classification literature has expanded rapidly over the last
decade, with many new classification approaches published each year. The
research focus has mostly been on improving the accuracy and efficiency of
classifiers, while their interpretability has been somewhat neglected.
Classifier interpretability has become a critical constraint for many
application domains and the introduction of the 'right to explanation' GDPR EU
legislation in May 2018 is likely to further emphasize the importance of
explainable learning algorithms. In this work we analyse the state-of-the-art
for time series classification, and propose new algorithms that aim to maintain
the classifier accuracy and efficiency, but keep interpretability as a key
design constraint. We present new time series classification algorithms that
advance the state-of-the-art by implementing the following three key ideas: (1)
Multiple resolutions of symbolic approximations: we combine symbolic
representations obtained using different parameters; (2) Multiple domain
representations: we combine symbolic approximations in time (e.g., SAX) and
frequency (e.g., SFA) domains; (3) Efficient navigation of a huge
symbolic-words space: we adapt a symbolic sequence classifier named SEQL, to
make it work with multiple domain representations (e.g., SAX-SEQL, SFA-SEQL),
and use its greedy feature selection strategy to effectively filter the best
features for each representation. We show that a multi-resolution multi-domain
linear classifier, SAX-SFA-SEQL, achieves a similar accuracy to the
state-of-the-art COTE ensemble, and to a recent deep learning method (FCN), but
uses a fraction of the time required by either COTE or FCN. We discuss the
accuracy, efficiency and interpretability of our proposed algorithms. To
further analyse the interpretability aspect of our classifiers, we present a
case study on an ecology benchmark.
