Оптимізаційна нейронна мережа для обробки часових рядів

The article proposes the architecture of the optimization neural network and the model of test sample synthesis for the process of extrapolation of time series parameters. In particular, the addition of an input layer with the introduction of an optimization scheme of nonlinear trade-offs has been implemented. Extrapolation of the behavior of the time series was carried out according to a test sample, which is formed as a data model with the selection of the trend according to the method of least squares. The scientific novelty of the results obtained in the article is reflected in the essence of these decisions. The aim of the research is to develop an optimization network architecture and data model for extrapolation, which allows to improve the accuracy and time of predicting the behavior of the time series outside the observation interval. Subject of research: architecture of an artificial neural network and methods of extrapolation of time series. Object of research: processes of architectural synthesis of an artificial neural network and extrapolation of time series behavior outside the observation interval. The optimization layer provides mini-requirements for the approximation of training and test samples. This is especially appropriate for time series with stochastic noise and allows you to reduce the impact of random errors on time series prediction results. The use of model data for extrapolation allows you to determine the behavior of the time series outside the observation interval. At the same time, the forecasting time with acceptable accuracy characteristics increases. These solutions are reflected in the name of the optimization neural network, which is proposed by the authors. The study of the effectiveness of the proposed solutions was implemented by methods of simulation modeling on a modified artificial neural network. The results of the calculations proved an increase in the adequacy of data models and an increase in the accuracy of extrapolation.