Abstract:

The research is devoted to the models and methods of determining the numeric evaluation of the object. A fuzzy model of the problem was built that allows presenting the results of experts surveys as the intervals of change in the numeric evaluation including determining the degrees of the confidence of the experts in their opinions. Heuristics were proposed to determine those experts from an expert group, the degrees of confidence of who have certain features in their assessments. The application of such mechanism allows excluding the experts who are not confident in their opinions or who display equal confidence in all the values of the intervals, determined by them. Rules of determining collective numeric evaluation of objects in the fuzzy problem of the numeric evaluation have been developed that are based on the simultaneous consideration of competence degrees of the experts as well as the degrees of their confidence in their assessments.  A forecasting model of time series was constructed, based on the fuzzy methods of determining collective numeric object assessment that provides a possibility to consider non-systematic and poor formalized external factors. The method of simultaneous usage of classic methods of forecasting and an “expert block” was designed to solve the problem of time series forecasting that provides a possibility to include the external factors to the forecasting value which have a high impact on the forecasting value but which have not systematic character or cannot be formalized. The efficiency and expedience of application of the proposed approaches to solving the practical problems of time series forecasting were proven, on the example of the task of forecasting quantitative characteristics of HIV–infected people, registered officially in the region.  Author Biographies Oksana Mulesa, Uzhgorod National University Narodna sq., 3, Uzhhorod, Ukraine, 88000 PhD, Associate Professor Department of cybernetics and applied mathematics

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