  Classification of time series is a growing problem in different disciplines
due to the progressive digitalization of the world. Currently, the
state-of-the-art in time series classification is dominated by The Hierarchical
Vote Collective of Transformation-based Ensembles. This algorithm is composed
of several classifiers of different domains distributed in five large modules.
The combination of the results obtained by each module weighed based on an
internal evaluation process allows this algorithm to obtain the best results in
state-of-the-art. One Nearest Neighbour with Dynamic Time Warping remains the
base classifier in any time series classification problem for its simplicity
and good results. Despite their performance, they share a weakness, which is
that they are not interpretable. In the field of time series classification,
there is a tradeoff between accuracy and interpretability. In this work, we
propose a set of characteristics capable of extracting information on the
structure of the time series to face time series classification problems. The
use of these characteristics allows the use of traditional classification
algorithms in time series problems. The experimental results of our proposal
show no statistically significant differences from the second and third best
models of the state-of-the-art. Apart from competitive results in accuracy, our
proposal is able to offer interpretable results based on the set of
characteristics proposed
