Time Series Classification (TSC) involves 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 the time series for a single case has multiple dimensions. Despite this, much less consideration has been given to MTSC than the univariate case. The UCR archive has provided a valuable resource for univariate TSC, and the lack of a standard set of test problems may explain why there has been less focus on MTSC. 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. If an algorithm cannot naturally handle multivariate data, the simplest approach to adapt a univariate classifier to MTSC is to ensemble it 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 four classifiers are significantly more accurate than the benchmark dynamic time warping algorithm and that one of these recently proposed classifiers, ROCKET, achieves significant improvement on the archive datasets in at least an order of magnitude less time than the other three.

时间序列分类 (TSC) 涉及根据有序的实值属性为离散目标变量构建预测模型。近年来，已经开发了一组新的 TSC 算法，这些算法比以前的技术水平有了显着的改进。主要关注于单变量 TSC，即每个案例具有单个系列和类标签的问题。实际上，更常见的是遇到多变量 TSC (MTSC) 问题，其中单个案例的时间序列具有多个维度。尽管如此，与单变量情况相比，对 MTSC 的考虑要少得多。UCR 档案为单变量 TSC 提供了宝贵的资源，缺乏一套标准的测试问题可以解释为什么对 MTSC 的关注较少。UEA 2018 年发布的 30 个 MTSC 问题档案使算法的比较变得更加容易。我们回顾了最近提出的基于深度学习、shapelets 和词袋方法的定制 MTSC 算法。如果算法不能自然地处理多变量数据，则使单变量分类器适应 MTSC 的最简单方法是将其集成到多变量维度上。我们在 30 个 MTSC 归档问题中的 26 个数据长度相同的情况下，将定制算法与这些与维度无关的方法进行了比较。我们证明了四个分类器比基准动态时间扭曲算法更准确，并且这些最近提出的分类器之一，ROCKET，在存档数据集上实现了显着改进，至少比其他三个分类器少一个数量级的时间。