  Time Series Classification (TSC) covers the supervised learning problem where
input data is provided in the form of series of values observed through
repeated measurements over time, and whose objective is to predict the category
to which they belong. When the class values are ordinal, classifiers that take
this into account can perform better than nominal classifiers. Time Series
Ordinal Classification (TSOC) is the field covering this gap, yet unexplored in
the literature. There are a wide range of time series problems showing an
ordered label structure, and TSC techniques that ignore the order relationship
discard useful information. Hence, this paper presents a first benchmarking of
TSOC methodologies, exploiting the ordering of the target labels to boost the
performance of current TSC state-of-the-art. Both convolutional- and deep
learning-based methodologies (among the best performing alternatives for
nominal TSC) are adapted for TSOC. For the experiments, a selection of 18
ordinal problems from two well-known archives has been made. In this way, this
paper contributes to the establishment of the state-of-the-art in TSOC. The
results obtained by ordinal versions are found to be significantly better than
current nominal TSC techniques in terms of ordinal performance metrics,
outlining the importance of considering the ordering of the labels when dealing
with this kind of problems.

Machine Learning