Convolutional and Deep Learning based techniques for Time Series Ordinal Classification
Convolutional and Deep Learning based techniques for Time Series Ordinal Classification
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DOI：
10.48550/arxiv.2306.10084
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Rafael Ayll'on-Gavil'an;David Guijo-Rubio;Pedro-Antonio Guti'errez;A. Bagnall;C. Herv'as-Mart'inez
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文献类型：
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作者：
Rafael Ayll'on-Gavil'an;David Guijo-Rubio;Pedro-Antonio Guti'errez;A. Bagnall;C. Herv'as-Mart'inez
时间序列分类（TSC）涵盖了监督学习问题，其中输入数据以通过随时间重复测量观察到的一系列值的形式提供，其目标是预测它们所属的类别。当类值是序数时，考虑到这一点的分类器可以比名义分类器表现得更好。时间序列有序分类（TSOC）是弥补这一差距的领域，但在文献中尚未探索。有一个有序的标签结构的时间序列问题的范围很广，和TSC技术，忽略了顺序关系丢弃有用的信息。因此，本文提出了TSOC方法的第一个基准测试，利用目标标签的排序来提高当前TSC最先进的性能。基于卷积和深度学习的方法（标称TSC的最佳替代方案之一）都适用于TSOC。对于实验，从两个著名的档案中选择了18个序数问题。通过这种方式，本文有助于建立国家的最先进的TSOC。通过序数版本获得的结果被发现是显着优于目前的标称TSC技术的序数性能指标，概述了考虑排序的标签时，处理这种问题的重要性。
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.