  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.
