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Deep learning for time series classification: a review

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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.

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Notes

  1. The implementations are available on https://github.com/hfawaz/dl-4-tsc.

  2. www.github.com/hfawaz/dl-4-tsc.

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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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  1. IRIMAS, Université Haute Alsace, Mulhouse, France

    Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar & Pierre-Alain Muller

  2. Faculty of IT, Monash University, Melbourne, Australia

    Germain Forestier

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Correspondence to Hassan Ismail Fawaz.

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Ismail Fawaz, H., Forestier, G., Weber, J. et al. Deep learning for time series classification: a review. Data Min Knowl Disc 33, 917–963 (2019). https://doi.org/10.1007/s10618-019-00619-1

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