International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

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:: International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

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ISSN 2228-9860
eISSN 1906-9642
CODEN: ITJEA8


FEATURE PEER-REVIEWED ARTICLE

Vol.12(3) (2021)

  • Statistics Application of the Dynamics Socio-Economic Processes: A Case of Russian Insurance Data

    Alfira Kumratova, Elena Popova (Department of Information systems, Kuban State Agricultural University named after I.T. Trubilin, RUSSIA),
    Elena Khudyakova (Department of Applied Informatics, Russian State Agrarian University Moscow Agricultural Academy named after K.A. Timiryazeva, Moscow, RUSSIA),
    Igor Vasilenko, Victor Saykinov (Department of Information systems, Kuban State Agricultural University named after I.T. Trubilin, RUSSIA).

    Disciplinary: Information Systems, Application, and Analysis, Mathematics.

    ➤ FullText

    doi: 10.14456/ITJEMAST.2021.51

    Keywords: Statistical indicators; Pre-predictive analysis; R/S analysis; risk criteria; Hurst exponent; Insurance time series; Insurance risk.

    Abstract
    This article demonstrates the work of the tool for forecasting the dynamics of socio-economic time series, data from insurance companies based on the complex use of both classical and nonlinear statistics. To obtain pre-forecast information about the time series, the authors proposed an analysis of classical statistical coefficients (kurtosis, asymmetry, and variation). Thus, a multi-criteria assessment of the stability of the dynamics of time series is presented. The methods of nonlinear dynamics adapted by the authors are proposed to be used in a multi-criteria (two-criteria) mathematical model. The result of the model's operation is an assessment of the trend stability of the time series. The first criterion reflects the time series's memory depth in the form of a fuzzy set obtained based on the R/S-analysis. The second criterion is the Hurst exponent. A two-criteria approach to assessing the trend stability of time series makes it possible to differentiate them according to the trend stability indicator and select working forecast models.

    Paper ID: 12A3I

    Cite this article:

    Kumratova, A., Popova, E., Khudyakova, E., Vasilenko, I., Saykinov, V. (2021). Statistics Application of the Dynamics Socio-Economic Processes: A Case of Russian Insurance Data. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 12(3), 12A3I, 1-8. http://doi.org/10.14456/ITJEMAST.2021.51



References

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  2. Perepelitsa, V. A., Popova, E. V., Komissarova K. A. (2007). Modeling the activity of insurance companies by methods of nonlinear dynamics: monograph. Scientific edition. Krasnodar: KubGAU, 201.
  3. Yangishieva, A. M. (2003). Alpha-fractal. Certificate of registration of the computer program. RU 2003611093, 08.05.2003. Application No. 2003610565 dated March 13.
  4. Yangishieva, A. M. (2005). Modeling economic risks by methods of nonlinear dynamics. Based on the materials of the Karachay-Cherkess Republic). Abstract of the dissertation of a candidate for economics. Sciences SSU, Stavropol, 24.
  5. Krichevsky, M. L. (2005). Intellectual methods in management. SPb. Peter, 305.
  6. Kumratova, A. M. Popova, E. V., Kurnosova, N. S., Popova M. I. (2015). Economic risk reduction based on pre-forecast analysis. Modern Economy: Problem and Solution, 3(63), 18-28.
  7. Kumratova, A. M. Popova, E. V., Tretyakov N. V. (2014). Methods of multicriteria optimization and classical statistics for the assessment of extreme risk values. Bulletin of the Kuban State University. Natural Sciences, 1, 55-60.
  8. Kumratova, A. M., Popova, E. V., Piterskaya, L. Y. (2019). Application of nonlinear dynamics methods for predictive testing the economic time series data. Indo American Journal of Pharmaceutical Science, 6(3), 5598-5602.
  9. Kumratova, A. A., Popova, E., Costa, Luis de Sousa. (2019). Hybrid approach of fractal and lin-guistic forecasting of winter wheat yields in southern Russia. Indo American journal of pharmaceutical science, 6(3), 5299-5303.
  10. Kumratova, A., Latysheva, L., Piterskaya, L., Shamrina, S., Kumratova, A., Popova, E. (2018). Systemic Inflationary Risk As A Factor To Investment Climate Formation. Research Journal of Pharmaceutical, Biological & Chemical Science, 9(6), 776-779.
  11. Kumratova, A., Bogoviz, A., Savinskaya, D., Kumratova, A. (2018). Expected Scenarios of Development of Information Economy in the Global Economic System. Models of modern information economy: conceptual contradictions & practical examples, 303-312.
  12. Kumratova, A., Popova. E., Costa. L., Zamotajlova, D. (2019). Methods of nonlinear dynamics as a hybrid tool for predic-tive analysis and research of risk-extreme levels. International Journal of Hybrid Intelligent System, 15(4), 221-241.
  13. Kumratova, A., Popova, E., Costa, L. de S. (2019). Hybrid instrumental means of predic-tive analysis of the dynamics of natural and economic processes. Advances in Intelligent Systems & Computing, 923, 31-39.
  14. Kumratova, A. Popova, E., Temirova, L., Shaposhnikova, O. (2019). Forecasting development of econom-ic processes using adapted nonlinear dynamics methods. International Journal of Engineering & Advanced Technology. 9(1), 3082-3085.


Other issues:
Vol.12(2)(2021)
Vol.12(1)(2021)
Vol.11(16)(2020)
Vol.11(15)(2020)
Vol.11(14)(2020)
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