Machine learning for noisy multivariate time series classification: a comparison and practical evaluation

  • Aldomar Pietro Santana Silva USP
  • Lucas Riera Abbade USP
  • Rodrigo da Silva Cunha USP
  • Tomaz Maia Suller USP
  • Eric Gomes USP
  • Edson Satoshi Gomi USP
  • Anna Helena Reali Costa USP


Multivariate Time Series Classification (MTSC) is a complex problem that has seen great advances in recent years from the application of state-of-the-art machine learning techniques. However, there is still a need for a thorough evaluation of the effect of signal noise in the classification performance of MTSC techniques. To this end, in this paper, we evaluate three current and effective MTSC classifiers – DDTW, ROCKET and InceptionTime – and propose their use in a real-world classification problem: the detection of mooring line failure in offshore platforms. We show that all of them feature state-of-the-art accuracy, with ROCKET presenting very good results, and InceptionTime being marginally more accurate and resilient to noise.


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SILVA, Aldomar Pietro Santana; ABBADE, Lucas Riera; CUNHA, Rodrigo da Silva; SULLER, Tomaz Maia; GOMES, Eric; GOMI, Edson Satoshi; COSTA, Anna Helena Reali. Machine learning for noisy multivariate time series classification: a comparison and practical evaluation. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 19. , 2022, Campinas/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 682-693. ISSN 2763-9061. DOI: