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Forecasting with Using Quasilinear Recurrence Equation

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Advances in Optimization and Applications (OPTIMA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1739))

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Abstract

We developed a new approach to the analysis of time series based on the use of quasi-linear recurrence relations. Unlike neural networks, this approach makes it possible to explicitly obtain high-quality quasi-linear difference equations (adequately describing the considered process). Currently, we developed and tested methods for identifying the parameters of a single equation. The research considers the identification algorithm for parameters of quasilinear recurrence equation. We use it to solve the problem of regression analysis with mutually dependent observable variables, which allows to implement the generalized last deviations method (GLDM). Using this model we held the computational experiment. The model using the identified parameters allows to obtain the long-time forecast.

The work was supported by Act 211 Government of the Russian Federation, contract No. 02.A03.21.0011. The work was supported by the Ministry of Science and Higher Education of the Russian Federation (government order FENU-2020-0022).

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Authors and Affiliations

  1. South Ural State University, Chelyabinsk, Russia

    Anatoly Panyukov, Tatiana Makarovskikh & Mostafa Abotaleb

Authors
  1. Anatoly Panyukov
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  2. Tatiana Makarovskikh
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  3. Mostafa Abotaleb
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Corresponding author

Correspondence to Tatiana Makarovskikh .

Editor information

Editors and Affiliations

  1. FRC CSC RAS, Moscow, Russia

    Nicholas Olenev

  2. FRC CSC RAS, Moscow, Russia

    Yuri Evtushenko

  3. University of Montenegro, Podgorica, Montenegro

    Milojica Jaćimović

  4. Krasovsky Institute of Mathematics and Mechanics, Ekaterinburg, Russia

    Michael Khachay

  5. FRC CSC RAS, Moscow, Russia

    Vlasta Malkova

  6. FRC CSC RAS, Moscow, Russia

    Igor Pospelov

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Panyukov, A., Makarovskikh, T., Abotaleb, M. (2022). Forecasting with Using Quasilinear Recurrence Equation. In: Olenev, N., Evtushenko, Y., Jaćimović, M., Khachay, M., Malkova, V., Pospelov, I. (eds) Advances in Optimization and Applications. OPTIMA 2022. Communications in Computer and Information Science, vol 1739. Springer, Cham. https://doi.org/10.1007/978-3-031-22990-9_13

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  • DOI: https://doi.org/10.1007/978-3-031-22990-9_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-22989-3

  • Online ISBN: 978-3-031-22990-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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