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Application of Wavelet Transform for Machine Learning Classification of Time Series

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Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making (ISDMCI 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 149))

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Abstract

The work is devoted to the time series classification based on the application of continuous wavelet transform and visualization of the resulting wavelet spectrum. Wavelet spectrum images are input to a neural network that classifies them. Wavelet transform allows one to analyze the time variation of frequency components of time series. The paper considers the classification of time realizations subject to normal additive noise with different variances. The wavelet spectrum visualization for various wavelet functions is presented. A residual neural network was used for the classification of the spectra images. The computational experiment results give evidence that the classification based on the recognition of wavelet spectra images allows qualitative distinguishing signals with an additive noise component having different signal-to-noise levels. Thus, we recommend applying the proposed method for classifying noisy time series of different types, such as medical and biological signals, financial time series, information traffic and others.

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Acknowledgements

The work was supported in part by Beethoven Grant No. DFG-NCN 2016/23/G/ST1/04083.

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Authors and Affiliations

  1. Kharkiv National University of Radio Electronics, Kharkiv, Ukraine

    Lyudmyla Kirichenko, Tamara Radivilova & Kyrylo Pavlenko

  2. Wroclaw University of Science and Technology, Wroclaw, Poland

    Lyudmyla Kirichenko

  3. National Aerospace University “Kharkiv Aviation Institute”, Kharkiv, Ukraine

    Oksana Pichugina

  4. University of Toronto, Toronto, Canada

    Oksana Pichugina

Authors
  1. Lyudmyla Kirichenko
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  2. Oksana Pichugina
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  3. Tamara Radivilova
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  4. Kyrylo Pavlenko
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Corresponding author

Correspondence to Oksana Pichugina .

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Editors and Affiliations

  1. Jan Evangelista Purkyně University in Ústi nad Labem, Ústi nad Labem, Czech Republic

    Sergii Babichev

  2. Kherson National Technical University, Kherson, Ukraine

    Volodymyr Lytvynenko

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Kirichenko, L., Pichugina, O., Radivilova, T., Pavlenko, K. (2023). Application of Wavelet Transform for Machine Learning Classification of Time Series. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making. ISDMCI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 149. Springer, Cham. https://doi.org/10.1007/978-3-031-16203-9_31

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