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Improving of the Identification Algorithm for a Quasilinear Recurrence Equation

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Advances in Optimization and Applications (OPTIMA 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1340))

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Abstract

Identification of quasilinear recurrence equations (QRE) may be reduced to the problem of regression analysis with mutually dependent observable variables. It is possible to use the generalized least deviations method (GLDM) for such problems. GLDM-estimation consists of solving the sequence of the WLDM-estimation problems. We propose the algorithm to solve the WLDM-estimation problem. Computational complexity of this algorithm does not exceed the quantity \(O(N^2T+T^2)\), where N is the number of coefficients in the considered equation, T is the number of observed readings. The computational complexity of solving practical GLDM estimation problems does not exceed \(O(N^3T+NT^2)\). Results of computational experiments to solve the problem of identifying the recurrence equation of the stock market index in Iraq by original data from the site “ISX-IQ.net” are presented. This results show the possibility to apply a second order quasilinear recurrence equation with quadratic nonlinearity for these purposes. Perhaps increasing the order of the recurrence equation and the accuracy of the calculations give better results.

The work is supported by Act 211 Government of the Russian Federation, contract No. 02.A03.21.0011.

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Authors and Affiliations

  1. Federal State Autonomous Educational Institution of Higher Education, South Ural State University (National Research University), 76, Lenin Prospekt, 454080, Chelyabinsk, Russia

    Anatoly V. Panyukov & Yasir Ali Mezaal

Authors
  1. Anatoly V. Panyukov
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  2. Yasir Ali Mezaal
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Correspondence to Anatoly V. Panyukov .

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Editors and Affiliations

  1. FRC CSC RAS, Moscow, Russia

    Nicholas Olenev

  2. FRC CSC RAS, Moscow, Russia

    Yuri Evtushenko

  3. Krasovsky Institute of Mathematics and Mechanics, Yekaterinburg, Russia

    Michael Khachay

  4. FRC CSC RAS, Moscow, Russia

    Vlasta Malkova

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Panyukov, A.V., Mezaal, Y.A. (2020). Improving of the Identification Algorithm for a Quasilinear Recurrence Equation. In: Olenev, N., Evtushenko, Y., Khachay, M., Malkova, V. (eds) Advances in Optimization and Applications. OPTIMA 2020. Communications in Computer and Information Science, vol 1340. Springer, Cham. https://doi.org/10.1007/978-3-030-65739-0_2

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  • DOI: https://doi.org/10.1007/978-3-030-65739-0_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-65738-3

  • Online ISBN: 978-3-030-65739-0

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