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Regression Spline-Model in Machine Learning for Signal Prediction and Parameterization

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Lecture Notes in Computational Intelligence and Decision Making (ISDMCI 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1020))

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Abstract

Traditionally, polynomial models are used to analyze and simulate time series. The parameters of these models are estimated using the ordinary least squares (OLS) method. The complex form of the time series requires the complication of the polynomial and the growth of its order. This leads to problems with the conditionality of the equations and the inadequacy of the model. Applying splines allows you to partially solve these problems. In this paper it is proposed to use cubic Hermite splines with infinite first and last fragments to analyze and predict time series, which allows using spline to predict. To estimate the spline parameters, the least squares method is used. For placement of nodes (docking points) algorithms of coordinate optimization with constraints and algorithm of sequential buildup of fragments are offered. In order to avoid over-training or under-training of the spline model, randomness control of residues is proposed. The results are applied for parameterization of heart sounds.

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Author information

Authors and Affiliations

  1. Kryvyi Rih Economic Institute of Kyiv National Economic University, 16, Medychna Street, Kryvyi Rih, Ukraine

    Ihor Shelevytsky & Ievgen Bykov

  2. Dnipropetrovsk Medical Academy, 9, Vernadsky Street, Dnipro, Ukraine

    Victoriya Shelevytska

  3. National Aviation University, 1, Komarova Street, Kyiv, Ukraine

    Kseniia Semenova

Authors
  1. Ihor Shelevytsky
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  2. Victoriya Shelevytska
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  3. Kseniia Semenova
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  4. Ievgen Bykov
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Corresponding authors

Correspondence to Ihor Shelevytsky , Victoriya Shelevytska , Kseniia Semenova or Ievgen Bykov .

Editor information

Editors and Affiliations

  1. Kherson National Technical University, Kherson, Ukraine

    Volodymyr Lytvynenko

  2. Jan Evangelista Purkyně University in Ústi and Labem, Usti and Labem, Czech Republic

    Sergii Babichev

  3. Lublin University of Technology, Lublin, Poland

    Waldemar Wójcik

  4. IT Step University, Lviv, Ukraine

    Olena Vynokurova

  5. Kherson National Technical University, Kherson, Ukraine

    Svetlana Vyshemyrskaya

  6. Kherson National Technical University, Kherson, Ukraine

    Svetlana Radetskaya

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Shelevytsky, I., Shelevytska, V., Semenova, K., Bykov, I. (2020). Regression Spline-Model in Machine Learning for Signal Prediction and Parameterization. In: Lytvynenko, V., Babichev, S., Wójcik, W., Vynokurova, O., Vyshemyrskaya, S., Radetskaya, S. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2019. Advances in Intelligent Systems and Computing, vol 1020. Springer, Cham. https://doi.org/10.1007/978-3-030-26474-1_12

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