IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0254841.html
   My bibliography  Save this article

An empirical survey of data augmentation for time series classification with neural networks

Author

Listed:
  • Brian Kenji Iwana
  • Seiichi Uchida

Abstract

In recent times, deep artificial neural networks have achieved many successes in pattern recognition. Part of this success can be attributed to the reliance on big data to increase generalization. However, in the field of time series recognition, many datasets are often very small. One method of addressing this problem is through the use of data augmentation. In this paper, we survey data augmentation techniques for time series and their application to time series classification with neural networks. We propose a taxonomy and outline the four families in time series data augmentation, including transformation-based methods, pattern mixing, generative models, and decomposition methods. Furthermore, we empirically evaluate 12 time series data augmentation methods on 128 time series classification datasets with six different types of neural networks. Through the results, we are able to analyze the characteristics, advantages and disadvantages, and recommendations of each data augmentation method. This survey aims to help in the selection of time series data augmentation for neural network applications.

Suggested Citation

  • Brian Kenji Iwana & Seiichi Uchida, 2021. "An empirical survey of data augmentation for time series classification with neural networks," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-32, July.
  • Handle: RePEc:plo:pone00:0254841
    DOI: 10.1371/journal.pone.0254841
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0254841
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0254841&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0254841?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Asif Khan & Hyunho Hwang & Heung Soo Kim, 2021. "Synthetic Data Augmentation and Deep Learning for the Fault Diagnosis of Rotating Machines," Mathematics, MDPI, vol. 9(18), pages 1-26, September.
    2. Vamsi K. Potluru & Daniel Borrajo & Andrea Coletta & Niccol`o Dalmasso & Yousef El-Laham & Elizabeth Fons & Mohsen Ghassemi & Sriram Gopalakrishnan & Vikesh Gosai & Eleonora Kreav{c}i'c & Ganapathy Ma, 2023. "Synthetic Data Applications in Finance," Papers 2401.00081, arXiv.org, revised Mar 2024.
    3. Hasan Tercan & Tobias Meisen, 2022. "Machine learning and deep learning based predictive quality in manufacturing: a systematic review," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 1879-1905, October.
    4. Oliver M. Crook & Kelsey Lane Warmbrod & Greg Lipstein & Christine Chung & Christopher W. Bakerlee & T. Greg McKelvey & Shelly R. Holland & Jacob L. Swett & Kevin M. Esvelt & Ethan C. Alley & William , 2022. "Analysis of the first genetic engineering attribution challenge," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    5. Minati, Ludovico & Li, Chao & Bartels, Jim & Chakraborty, Parthojit & Li, Zixuan & Yoshimura, Natsue & Frasca, Mattia & Ito, Hiroyuki, 2023. "Accelerometer time series augmentation through externally driving a non-linear dynamical system," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    6. Zhaoyan Liu & Min Shu & Wei Zhu, 2024. "Contrastive Learning Framework for Bitcoin Crash Prediction," Stats, MDPI, vol. 7(2), pages 1-32, May.
    7. Chengguang Liu & Jiaqi Zhang & Xixi Luo & Yulin Yang & Chao Hu, 2023. "Railway Freight Demand Forecasting Based on Multiple Factors: Grey Relational Analysis and Deep Autoencoder Neural Networks," Sustainability, MDPI, vol. 15(12), pages 1-21, June.
    8. Li, Ding & Zhang, Yufei & Yang, Zheng & Jin, Yaohui & Xu, Yanyan, 2024. "Sensing anomaly of photovoltaic systems with sequential conditional variational autoencoder," Applied Energy, Elsevier, vol. 353(PA).
    9. Elalem, Yara Kayyali & Maier, Sebastian & Seifert, Ralf W., 2023. "A machine learning-based framework for forecasting sales of new products with short life cycles using deep neural networks," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1874-1894.
    10. Kong, Yun & Han, Qinkai & Chu, Fulei & Qin, Yechen & Dong, Mingming, 2023. "Spectral ensemble sparse representation classification approach for super-robust health diagnostics of wind turbine planetary gearbox," Renewable Energy, Elsevier, vol. 219(P1).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0254841. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.