Time Series Classification (TSC) involved building predictive models for a discrete target variable from ordered, real valued, attributes. Over recent years, a new set of TSC algorithms have been developed which have made significant improvement over the previous state of the art. The main focus has been on univariate TSC, i.e. the problem where each case has a single series and a class label. In reality, it is more common to encounter multivariate TSC (MTSC) problems where multiple series are associated with a single label. Despite this, much less consideration has been given to MTSC than the univariate case. The UEA archive of 30 MTSC problems released in 2018 has made comparison of algorithms easier. We review recently proposed bespoke MTSC algorithms based on deep learning, shapelets and bag of words approaches. The simplest approach to MTSC is to ensemble univariate classifiers over the multivariate dimensions. We compare the bespoke algorithms to these dimension independent approaches on the 26 of the 30 MTSC archive problems where the data are all of equal length. We demonstrate that the independent ensemble of HIVE-COTE classifiers is the most accurate, but that, unlike with univariate classification, dynamic time warping is still competitive at MTSC.

翻译：时间序列分类（TSC）涉及从有序实值属性中为离散目标变量构建预测模型。近年来，一系列TSC算法被开发出来，并显著超越了先前的最优水平。现有研究主要集中于单变量TSC问题，即每个样本仅包含单一时间序列及其类别标签。然而在实际应用中，多变量时间序列分类（MTSC）问题更为常见——多个时间序列共同关联一个标签。尽管如此，针对MTSC的研究远少于单变量情况。2018年发布的包含30个MTSC问题的UEA数据集为算法比较提供了便利。本文综述了近期提出的基于深度学习、shapelet以及词袋方法的定制化MTSC算法。处理MTSC最直接的方法是在多变量维度上集成单变量分类器。我们在30个MTSC数据集中26个等长序列问题上，将定制化算法与上述跨维度独立方法进行对比。实验表明，HIVE-COTE分类器的独立集成具有最高精度，但与单变量分类不同，动态时间规整在MTSC任务中仍具竞争力。