4.5 Review

The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances

Journal

DATA MINING AND KNOWLEDGE DISCOVERY
Volume 35, Issue 2, Pages 401-449

Publisher

SPRINGER
DOI: 10.1007/s10618-020-00727-3

Keywords

Time series classification; Evaluating classifiers; Multivariate time series; UEA archive

Funding

  1. Biotechnology and Biological Sciences Research Council [BB/M011216/1]
  2. UK Engineering and Physical Sciences Research Council (EPSRC) [EP/M015807/1]
  3. Research and Specialist Computing Support service at the University of East Anglia
  4. EPSRC [EP/M015807/1] Funding Source: UKRI

Ask authors/readers for more resources

Time Series Classification (TSC) has seen significant advancements in recent years, with more focus on univariate TSC compared to multivariate TSC. While there are valuable resources for univariate TSC, there is a lack of standard test problems for MTSC. Recent bespoke MTSC algorithms, including deep learning, shapelets and bag of words approaches, have shown promise in improving classification accuracy.
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.

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