Scientific Journal of KubSAU

The lack of a unified research platform and tools for various sectors of Russian economy, allowing to take into account the specifics of the object of study, significantly slows down and complicates the decisionmaking processes, at the same time thereby reducing their efficiency, which is even more negative in terms of the need of quick decisions of the tasks on import substitution. Scientific essence of the proposed research can be formulated in the form of innovative unified research platform, showing the interrelated causal system components, theoretical and practical, analytical and experimental units, productive activities which are scientifically proven smart products for various sectors of the Russian economy. The constantly changing economic environment makes to answer its idempotent mathematics and information paradigm, theory, methodology. Here it is important to select the structure and rationale of the proposed research mathematical "platform". A new, different but mutually complementary multi-criteria approaches, a set of economic-mathematical models and modern mathematical and instrumental constructs, monitoring, comparison, and generalization of the results is needed. In the article it is shown that the proposed use of instrumentation and mathematical methods represent essentially new base for forecasting of discrete evolutionary processes

Tools and mathematical methods offered for usage represent essentially new base for forecasting of discrete evolutionary processes. Authors represent complete system of models and methods of temporary ranks’ with memory forecasting

This work is devoted to the methods of multicriteria optimization and classical statistics of obtaining pre-estimated information for time series that have long-term memory, which is why their levels do not satisfy the independence property, and therefore the classical prediction methods may be inadequate. The developed methods of obtaining such information are based on classical statistics methods such as mathematical statistics, multicriteria optimization and extreme value theory. The effectiveness of the proposed approach has been demonstrated on the example of specific time series of volumes of mountain rivers

The article discusses the impact of seasonal and event-component time series to assess the predictive performance of the tourist flow in Dombay village in the Karachay-Cherkessia Republic

In the article the forecasting model which is based on the theory of cellular automatic machines and mathematical apparatus of indistinct sets is presented. Its work on the real data of time number productivities of sugar beet in Mostovskoy area of Krasnodar territory is shown

In the process of formation of nonlinear dynamics, the scientific society was able to refute the classical mechanisms of Newton-Laplace by justifying the chaotic nature of the phenomena of the world. However, despite the emergence of new mathematical models and tools, forecasting of nonlinear systems is a difficult task, as not only the quantitative and qualitative characteristics of the factors affecting the system are unknown, but also there is a problem of a small amount of information for forecasting. In this article, the authors consider the linear cellular apparatus as a tool for prediction the final state, to which the system will come based only on its output indicators of previous years. Since the use of a linear cellular automaton for prediction of nonlinear systems is an assumption of the authors, it should be tested on the series of stochastic systems exposed to different risk factors, which together give either a positive response of the system or a negative one. An example of such series is the time series of yields, as it is affected by climatic conditions, the appearance of which, in turn, is also difficult to predict. Prediction of stochastic systems using linear cellular automaton really makes it possible to get adequate and visual models. Due to the fact that the forecast model has a discrepancy with the real result of 0-15% (both positive and negative), the conclusion is that the predicted value will help either to take measures to ensure that the real value in the future is not lower, or to make sure that the decisions and measures taken are correct, when a value is higher than the forecast