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

Data mining and knowledge discovery, 2021-03, Vol.35 (2), p.401-449 [Peer Reviewed Journal]

The Author(s) 2020 ;The Author(s) 2020. ;The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1384-5810 ;EISSN: 1573-756X ;DOI: 10.1007/s10618-020-00727-3 ;PMID: 33679210

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  • Title:
    The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances
  • Author: Ruiz, Alejandro Pasos ; Flynn, Michael ; Large, James ; Middlehurst, Matthew ; Bagnall, Anthony
  • Subjects: Academic Surveys and Tutorials ; Algorithms ; Archives & records ; Artificial Intelligence ; Chemistry and Earth Sciences ; Classification ; Classifiers ; Computer Science ; Data Mining and Knowledge Discovery ; Information Storage and Retrieval ; Machine learning ; Multivariate analysis ; Physics ; Prediction models ; Statistics for Engineering ; Time series
  • Is Part Of: Data mining and knowledge discovery, 2021-03, Vol.35 (2), p.401-449
  • Description: 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.
  • Publisher: New York: Springer US
  • Language: English
  • Identifier: ISSN: 1384-5810
    EISSN: 1573-756X
    DOI: 10.1007/s10618-020-00727-3
    PMID: 33679210
  • Source: Springer Nature OA/Free Journals
    ProQuest Central
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