ПРОГНОЗНЕ МОДЕЛЮВАННЯ НЕЛІНІЙНИХ НЕСТАЦІОНАРНИХ ПРОЦЕСІВ У РОСЛИННИЦТВІ З ВИКОРИСТАННЯМ ІНСТРУМЕНТІВ SAS ENTERPRISE MINERBlackground. The issue of providing the increase of production of main agricultural crops in Ukraine under conditions of environmental management requires the use of modern scientific approaches. The complexity of solving this problem lies in the lack of practical experience of applying modern information-analytical systems, where different methods for analysis and modeling of nonlinear non-stationary processes in crop production would be implemented simultaneously. The proposed methodology has the advantage of using the tools of SAS Enterprise Miner -software where a wide range of techniques are implemented, that should be used for predictive modeling of main agricultural crops according to the performed research. Objective. The goal of the study is in application of the integrated methods of analysis and predictive modeling of non-stationary processes for agricultural crop yield prediction using SAS Enterprise Miner tools. Methods. To solve the problems stated the following approaches were used: systems analysis, regression analysis, gradient boosting, probabilistic modeling and decision trees. The methodology for developing of crop yield prediction under influence of various groups of factors was offered, and the possibility of their use in decision support systems in agriculture was substantiated. Results. Based on the analysis of the works of domestic and foreign scientists it was proposed to improve methodology of development of yield prediction of main agricultural crops using integrated analysis methods, which were implemented in the system of SAS Enterprise Miner. The analysis of the obtained results was performed. Conclusions. Winter wheat and corn yield prediction was performed for the Forest-Steppe Zone using the developed methodic. Different methods of construction of models for prediction of the non-stationary processes were applied; the choice of the worthiest one was reasonably proved. Advanced information technologies, including SAS Enterprise Miner, were used for automatization the process of selecting the optimal model for investigated crop yield prediction.

Background. The modern financial and economic processes and accompanying risks often exhibit sophisticated patterns, contain non-stationary and non-linear features that require development of special models for their description and forecasting. To solve the problem successfully it is helpful to construct appropriate decision support system using systemic principles. Objective. Development of decision support system architecture and its functional layout for economic and financial processes model constructing with statistical data as well as financial risk estimation. The system should help coping with possible uncertainties and implemented on the basis of modern information technologies. Methods. Mathematical modeling and forecasting techniques for financial and economic process; approaches to financial risks estimation using statistical data. The use of modern information technologies for practical implementation of decision support system. Results. Information technology and decision support system as a practical tool for modeling nonlinear non-stationary processes in economy and finances, as well as financial risk estimation were developed. Experimental results of statistical data processing prove the correctness of the approaches proposed. Conclusions. The systemic methodology is proposed and implemented for constructing decision support system for mathematical modeling and forecasting modern economic and financial processes as well as for financial risk estimation that is based on the following system analysis principles: hierarchical system structure, taking into consideration probabilistic and statistical uncertainties, availability of model adaptation features, generating multiple decision alternatives, and tracking of computational processes at all the stages of data processing with appropriate sets of statistical quality criteria.

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The article describes the original information technology of the algorithmic trading, designed to solve the problem of forming the optimal portfolio of trade strategies. The methodology of robust optimization, using the Ledoit–Wolf shrinkage method for obtaining stable estimates of the covariance matrix of algorithmic strategies, was used for the formation of a portfolio of trade strategies. The corresponding software was implemented by SAS OPTMODEL Procedure. The paper deals with a portfolio of trade strategies built for highly-profitable, but also highly risky financial tools—cryptocurrencies. Available bitcoin assets were divided into a corresponding proportion for each of the recommended portfolio strategies, and during the selected period (one calendar month) were used for this research. The portfolio of trade strategies is rebuilt at the end of the period (every month) based on the results of trade during the period, in accordance with the conditions of risk minimizing or income maximizing. Trading strategies work in parallel, being in a state of waiting for a relevant trading signal. Strategies can be changed by moving the parameters in accordance with the current state of the financial market, removed if ineffective, and replaced where necessary. The efficiency of using a robust decision-making method in the context of uncertainty regarding cryptocurrency trading was confirmed by the results of real trading for the Bitcoin/Dollar pair. Implementation of the offered information technology in electronic trading systems will allow risk reduction as a result of making incorrect decisions or delays in making decisions in a systemic trading.

This paper addresses to the problem of using SAS Enterprise Guide 6.1 as a means for building probabilistic models and as optimum method of modeling gross domestic product in terms of the economic crisis and social threats is proposed. Today in a complex socio-political and economic situation growing influence of external factors, presence of uncertainties and risks there exists a problem of anticipating potential threats in the humanitarian and social spheres and ways to overcome them aiming to provide food security and controllability of ecological situation. All these problems, as reported in the NATO program "Science for Peace and Security", are of high priority for the countries that need to take into account threats to security, including Ukraine. That is why in the framework of the project NUKR. SFPP G4877 "Modeling and Mitigation of Social Disasters Caused by Catastrophes and Terrorism" the problems of scientific prediction of national economy for the period to 2030 as one of the measures preventing growth of social tension in the country are disclosed.