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.

本研究通过建立 TSOC 基准测试来探讨序列分类问题。实验表明，相较于对同类问题的标准分类方法，TSOC 方法在有序标签结构的时间序列数据上的分类表现更好。

基于卷积和深度学习的时间序列有序分类技术

Convolutional and Deep Learning based techniques for Time Series Ordinal  Classification

Contrastive self-supervised learning has been successfully used in many
domains, such as images, texts, graphs, etc., to learn features without
requiring label information. In this paper, we propose a new local contrastive
feature learning (LoCL) framework, and our theme is to learn local
patterns/features from tabular data. In order to create a niche for local
learning, we use feature correlations to create a maximum-spanning tree, and
break the tree into feature subsets, with strongly correlated features being
assigned next to each other. Convolutional learning of the features is used to
learn latent feature space, regulated by contrastive and reconstruction losses.
Experiments on public tabular datasets show the effectiveness of the proposed
method versus state-of-the-art baseline methods.

本文提出了一种新的本地对比特征学习（LoCL）框架，其主题是从表格数据中学习本地模式 / 特征。利用特征相关性创建最大生成树并将其分解，使用卷积学习特征，受对比和重构损失控制，与其他领先方法相比在公共表格数据集上实验表明提出的方法的有效性。