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The improvement of the intelligent decision support system for forecasting non-linear non-stationary processes

Author

Listed:
  • Petro Bidiuk

    (National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute»)

  • Tetyana Prosyankina-Zharova

    (Institute of Telecommunications and Global Information Space of National Academy of Sciences of Ukraine)

  • Valerii Diakon

    (Institute of Telecommunications and Global Information Space of National Academy of Sciences of Ukraine)

  • Dmytro Diakon

    (Institute of Telecommunications and Institute of Telecommunications and Global Information Space of National Academy of Sciences of Ukraine)

Abstract

The paper is focused on solving the modern scientific and applied problem related to development and practical use in Decision Support Systems (DSS) of information technologies directed towards forecasting of non-linear non-stationary processes (NNP) that take place in economy and finances as well as in many other areas of activities. Thus, object of study are non-linear non-stationary processes taking place in economy and financial sphere.The basic problem of the study is development of new mathematical models and methods of analysis and forecasting non-linear non-stationary processes in economy and finances, improvement of information decision support technologies that would help to enhance quality of forecast estimates and respective decisions in conditions of uncertainties and risk. The methods given in the paper are used for automating the process of intellectual data analysis that describe the processes under study and automatizing model constructing procedures.As a result of the study performed the information technology was developed to be used in DSS based upon system analysis principles, taking into consideration possible data uncertainties, regression and intellectual data analysis. The technology provides for constructing adequate models of the process under study and computing high quality forecast estimates. The particular feature of the approach proposed is that it provides for high quality of experimental results due to taking into consideration special features of non-linear non-stationary processes that take place in various spheres of activities and their evolution is influenced by many specific factors.The use of the technology proposed in decision support systems of enterprises, state governmental organs, and local self-government will create basis for effective solving the tasks of governing development of non-linear non-stationary processes that take place in many spheres of activities. The approaches proposed in the paper can be used in practice as separately as well as parts of existing information systems at enterprises and organizations.

Suggested Citation

  • Petro Bidiuk & Tetyana Prosyankina-Zharova & Valerii Diakon & Dmytro Diakon, 2023. "The improvement of the intelligent decision support system for forecasting non-linear non-stationary processes," Technology audit and production reserves, PC TECHNOLOGY CENTER, vol. 4(2(72)), pages 37-46, August.
    • Handle: RePEc:baq:taprar:v:4:y:2023:i:2:p:37-46
    DOI: 10.15587/2706-5448.2023.286516
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    References listed on IDEAS

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    1. Sandrine Le Pontois & Marc Jaillot, 2021. "Activating Creativity in Situations of Uncertainty: The Role of Third Spaces," Journal of Innovation Economics, De Boeck Université, vol. 0(3), pages 33-62.
    2. Chan, Nigel & Wang, Qiying, 2015. "Nonlinear regressions with nonstationary time series," Journal of Econometrics, Elsevier, vol. 185(1), pages 182-195.
    3. Gheorghe RUXANDA & Sorin OPINCARIU & Stefan IONESCU, 2019. "Modelling Non-Stationary Financial Time Series with Input Warped Student T-Processes," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 51-61, September.
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