Abstract: The development of the tourism sector of the economy is one of the priorities set by the leadership of the Krasnodar Territory and the Russian Federation. Thus, the construction of a model of the influence of tourist flows on the stability of the territory is very relevant.
The object of the study is the interconnected economic, social and ecological system of the resort destination. The subject of the study is the interaction of key indicators affecting the sustainability of a tourist destination and tourist flows.
The work is aimed at building an integrated computer model that can be used for:
-studying the interaction of key variables;
- conducting scenario analysis and modeling to determine the possible consequences of management decisions.The authors develop a single synthetic model that combines social, economic and environmental aspects of the subject of research. This model is evaluated using an adjusted net savings indicator and allows us to study the trends in the development of the tourism sector, as well as to conduct a scenario analysis of the consequences of various management decisions. Using this tool for medium- and long-term planning will give the decision-maker more information in conditions of uncertainty, which will avoid many managerial mistakes. In the future, it is planned to refine and adjust the model using new statistical data; to conduct computational experiments to identify economic trends in the impact of integrated programs and scenarios for the development of the tourism sector.

Abstract: Monitoring and analysis of consumption of energy resources in various contexts, as well as measuring of parameters (indicators) in time are of utmost importance for the modern economy. This work is dedicated to examination and interpretation of the anomalies of collecting data on consumption of energy resources (on the example of gas consumption) in the municipal formation. Gas consumption is important for the socioeconomic sphere of cities. Unauthorized connections are the key reason for non-technological waste of the resource. The traditional methods of detection of stealing of gas are ineffective and time-consuming. The modern technologies of data analysis would allow detecting and interpreting the anomalies of consumption, as well as forming the lists for checking the objects for unauthorized connections. The author’s special contribution lies in application of the set of statistical methods aimed at processing and identification of anomalies in energy consumption of a municipal formation. It is worth noting that the use of such technologies requires the development of effective algorithms and implementation of automation and machine learning algorithms. The new perspective upon time-series data facilitates identification of anomalies, optimization of decision-making, etc. These processes can be automated. The presented methodology tested on time-series data that describes the consumption of gas can be used for a broader range of tasks. The research can be combined with the methods of knowledge discovery and deep learning algorithms.

Abstract: The authors propose to combine logical inference with the apparatus of fuzzy sets. When each solution is associated with a set of possible outcomes with known conditional probabilities, the solution is chosen based on digital information under conditions of uncertainty.
Therefore, the main purpose of using fuzzy logic in expert systems is to create computing devices (or software complexes) capable of simulating human thinking and explaining decision-making methods
The purpose of the work is to describe in detail a reproducible standard method of constructing rules for the output of an expert system for various economic subject areas, using a universal knowledge base scheme To make decisions in a fuzzy system, it is proposed to use the process of identifying the structure of a rule - determining the structural characteristics of a fuzzy system, such as the number of fuzzy rules, the number of linguistic terms into which incoming variables are divided. This identification is carried out using fuzzy cluster analysis, which is carried out using fuzzy decision trees. The authors present a block diagram of the inference methodology based on fuzzy logic. The method of constructing rules and the algorithm of fuzzy inference presented in the article can be used in various spheres of the economy. The novelty of the work lies in the automation and integration of the system for determining fuzzy inference rules with the stage of collecting input data in the subject area

Abstract: The goal of this research consists in the analysis of trends in a compositely structured index of the “quality of life”, and analysis of sensitivity of a complex indicator by separate factors and cross-section of population of Krasnodar Krai. Based on the acquired results, the author builds a mathematical economic model for the analysis and forecasting of changes in the quality of life of the population of Krasnodar Krai in the context of diverse clusters of municipal formations, as well as compares different functions of approximation. The subject of this research is the socioeconomic interaction within the regional system of Krasnodar Krai. The article carries out a retrospective analysis is conducted on the most important indexes characterizing the subject field, correlation-regression analysis of variables, and sensitivity analysis based on the coefficients of elasticity of private factors. The author’s main contribution into the research of this topic lies in building the regression models of the quality of life index of the population of Krasnodar Krai in the context of diverse social clusters, as wll as in determination of the degree of impact of separate social, demographic or economic factors upon complex indicator. Such models allow forecasting and carrying out experimental modeling in this area.

Abstract: The aim of the study is to prepare for the analysis of poorly structured source data, their analysis, the study of the influence of data "pollution" on the results of regression analysis. The task of structuring data, preparing them for a qualitative analysis is a unique task for each specific set of source data and cannot be solved using a general algorithm, it will always have its own characteristics. The problems that may cause difficulties when working (analysis, processing, search) with poorly structured data are considered. Examples of poorly structured data and structured data that are used in the preparation of data for analysis are given. These algorithms for preparing weakly structured data for analysis are considered and described. The cleaning and analysis procedures on the data set were carried out. Four regression models were constructed and compared. As a result, the following conclusions were formulated: Exclusion from the analysis of various kinds of suspicious observations can drastically reduce the size of the population and lead to an unreasonable decrease in variation. At the same time, such an approach would be completely unacceptable if, as a result, important objects of observation are excluded from the analysis and the integrity of the population is violated. The quality of the constructed model may deteriorate in the presence of abnormal values, but may also improve due to them.