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Data Mining and Knowledge Discovery

, Volume 33, Issue 4, pp 917–963 | Cite as

Deep learning for time series classification: a review

  • Hassan Ismail FawazEmail author
  • Germain Forestier
  • Jonathan Weber
  • Lhassane Idoumghar
  • Pierre-Alain Muller
Article
Part of the following topical collections:
  1. Academic Surveys and Tutorials
  2. Academic Surveys and Tutorials
  3. Academic Surveys and Tutorials

Abstract

Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learning has seen very successful applications in the last years. DNNs have indeed revolutionized the field of computer vision especially with the advent of novel deeper architectures such as Residual and Convolutional Neural Networks. Apart from images, sequential data such as text and audio can also be processed with DNNs to reach state-of-the-art performance for document classification and speech recognition. In this article, we study the current state-of-the-art performance of deep learning algorithms for TSC by presenting an empirical study of the most recent DNN architectures for TSC. We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC. We also provide an open source deep learning framework to the TSC community where we implemented each of the compared approaches and evaluated them on a univariate TSC benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By training 8730 deep learning models on 97 time series datasets, we propose the most exhaustive study of DNNs for TSC to date.

Keywords

Deep learning Time series Classification Review 

Notes

Acknowledgements

The authors would like to thank the creators and providers of the datasets: Hoang Anh Dau, Anthony Bagnall, Kaveh Kamgar, Chin-Chia Michael Yeh, Yan Zhu, Shaghayegh Gharghabi, Chotirat Ann Ratanamahatana, Eamonn Keogh and Mustafa Baydogan. The authors would also like to thank NVIDIA Corporation for the GPU Grant and the Mésocentre of Strasbourg for providing access to the cluster. The authors would also like to thank François Petitjean and Charlotte Pelletier for the fruitful discussions, their feedback and comments while writing this paper. This work was supported by the ANR TIMES project (Grant ANR-17-CE23-0015) of the French Agence Nationale de la Recherche.

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Copyright information

© The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.IRIMASUniversité Haute AlsaceMulhouseFrance
  2. 2.Faculty of ITMonash UniversityMelbourneAustralia

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