@article{Tymoshchuk_Huskova_Bidyuk_2019, title={A COMBINED APPROACH TO MODELING NONSTATIONARY HETEROSCEDASTIC PROCESSES}, url={http://ric.zntu.edu.ua/article/view/174465}, DOI={10.15588/10.15588/1607-3274-2019-2-9}, abstractNote={<p>Context. Nonlinear nonstationary processes are observed today in various fields of studies: economy, finances, ecology, demography<br />etc. Very often special approaches are required for model development and forecasts estimation for the processes mentioned.<br />The modeling methodologies have to take into consideration possible uncertainties that are encountered during data processing and<br />model structure and parameter estimation.<br />Objective. To develop a modified methodology for constructing models for nonlinear processes that allows for achieving high<br />quality of forecasts. More specifically heteroscedastic processes are considered that create a wide class of nonlinear nonstationary<br />processes and are considered in many areas of research.<br />Method. To reach the aim of the study mentioned the following methods are used: systemic approach to model building and<br />forecasting, modified methodology for modeling nonlinear processes, methods for identification and taking into consideration possible<br />uncertainties. To cope with the structural uncertainties following techniques: refinement of model order applying recurrent adaptive<br />approach to modeling and automatic search for the “best” structure using complex statistical criteria; adaptive estimation of input<br />delay time, and the type of data distribution with its parameters; describing detected nonlinearities with alternative analytical forms<br />with subsequent estimation of the forecasts generated.<br />Results. The proposed modified methodology for modeling nonlinear nonstationary processes, adaptation scheme for model<br />building, new model structures proposed. As a result of performing computational experiments, it was found that nonlinear models<br />constructed provide a possibility for computing high quality forecasts for the process under study and their variance.<br />Conclusions. Application of the modeling methodology proposed provides a possibility for structural and parametric adaptation<br />of the models constructed with statistical data. The models developed exhibit acceptable adequacy and quality of short-term forecasting.</p>}, number={2}, journal={Radio Electronics, Computer Science, Control}, author={Tymoshchuk, O. L. and Huskova, V. H. and Bidyuk, P. I.}, year={2019}, month={May}, pages={80–89} }