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

Pasos Ruiz, Alejandro, Flynn, Michael, Large, James, Middlehurst, Matthew and Bagnall, Anthony (2021) The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Mining and Knowledge Discovery, 35. 401–449. ISSN 1384-5810

[thumbnail of Ruiz2020_Article_TheGreatMultivariateTimeSeries]
PDF (Ruiz2020_Article_TheGreatMultivariateTimeSeries) - Published Version
Available under License Creative Commons Attribution.

Download (3MB) | Preview


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.

Item Type: Article
Uncontrolled Keywords: evaluating classifiers,multivariate time series,time series classification,uea archive,information systems,computer science applications,computer networks and communications ,/dk/atira/pure/subjectarea/asjc/1700/1710
Faculty \ School: Faculty of Science > School of Computing Sciences
UEA Research Groups: Faculty of Science > Research Groups > Data Science and Statistics
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 26 Nov 2020 00:49
Last Modified: 22 Mar 2024 09:30
DOI: 10.1007/s10618-020-00727-3


Downloads per month over past year

Actions (login required)

View Item View Item