ADAPTIVE MODELING AND FORECASTING OF NONLINEAR NONSTATIONARY PROCESSES

Authors

  • P. I. Bidyuk National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”
  • V. M. Sineglazov National Aviation University, Kyiv

DOI:

https://doi.org/10.18372/1990-5548.63.14515

Keywords:

Adaptive modeling, probabilistic and statistical models, short-term forecasting, uncertainties in modeling, system analysis principles

Abstract

The study is directed towards development of adaptive decision support system for modeling and forecasting nonlinear nonstationary processes in economy, finances and other areas of human activities. The structure and parameter adaptation procedures for the regression and probabilistic models are proposed as well as respective information system architecture and functional layout are developed. The system development is based on the system analysis principles such as adaptive model structure estimation, optimization of model parameter estimation procedures, identification and taking into consideration of possible uncertainties met in the process of data processing and mathematical model development. The uncertainties are inherent to data collecting, model constructing and forecasting procedures and play a role of negative influence factors to the information system computational procedures. Reduction of their influence is favorable for enhancing the quality of intermediate and final results of computations. The illustrative examples of practical application of the system developed proving the system functionality are provided.

Author Biographies

P. I. Bidyuk, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”

Institute for Applied System Analysis

Doctor of Engineering Science. Professor

orcid.org/0000-0002-7421-3565

V. M. Sineglazov, National Aviation University, Kyiv

Aviation Computer-Integrated Complexes Department

Doctor of Engineering Science. Professor. Head of the Department

orcid.org/0000-0002-3297-9060.

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Issue

Vol. 1 No. 63 (2020)

Section

COMPUTER SCIENCES AND INFORMATION TECHNOLOGIES

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