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Time Series Classification (TSC) covers the supervised learning problem whereinput data is provided in the form of series of values observed throughrepeated measurements over time, and whose objective is to predict the categoryto which they belong. When the class values are ordinal, classifiers that takethis into account can perform better than nominal classifiers. Time SeriesOrdinal Classification (TSOC) is the field covering this gap, yet unexplored inthe literature. There are a wide range of time series problems showing anordered label structure, and TSC techniques that ignore the order relationshipdiscard useful information. Hence, this paper presents a first benchmarking ofTSOC methodologies, exploiting the ordering of the target labels to boost theperformance of current TSC state-of-the-art. Both convolutional- and deeplearning-based methodologies (among the best performing alternatives fornominal TSC) are adapted for TSOC. For the experiments, a selection of 18ordinal problems from two well-known archives has been made. In this way, thispaper contributes to the establishment of the state-of-the-art in TSOC. Theresults obtained by ordinal versions are found to be significantly better thancurrent nominal TSC techniques in terms of ordinal performance metrics,outlining the importance of considering the ordering of the labels when dealingwith this kind of problems.

Convolutional and Deep Learning based techniques for Time Series Ordinal Classification