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 29 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.

翻译：时间序列分类（TSC）涵盖了一类监督学习问题，其输入数据以随时间重复测量观测到的一系列数值形式提供，目标在于预测其所属的类别。当类别取值具有序数性质时，考虑这一特性的分类器可能优于名义分类器。时间序列序数分类（TSOC）正是填补这一空白的研究领域，目前在文献中尚未得到充分探索。大量时间序列问题呈现出有序标签结构，而忽略顺序关系的TSC技术会损失有效信息。为此，本文首次对TSOC方法进行基准测试，利用目标标签的序数关系提升当前TSC最优技术的性能。研究将基于卷积和深度学习的方法（这些方法在名义TSC中表现最佳）适配至TSOC场景。实验选取了两个知名数据库中的29个序数分类问题。通过这种方式，本文为建立TSOC领域的研究基准作出贡献。实验结果表明，序数化版本在序数性能指标上显著优于当前名义TSC技术，这凸显了处理此类问题时考虑标签顺序的重要性。