A New Nonparametric Algorithm for Preprocessing Stochastic Data with Uncertainty

The article deals with the problem of modeling stochastic processes under uncertainty. The peculiarity of the processes under consideration is that the researcher does not have information about the mathematical structure of the object; the object is represented as a black box. The article proposes to use a nonparametric modeling algorithm based on a nonparametric estimate of the regression function on observations. To improve the accuracy of modeling, it is proposed to use an algorithm for generating training samples. The algorithm differs from the previous modification by the definition of essential variables. The results of computational experiments have shown the effectiveness of the proposed algorithms.