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Problems of dynamic modeling of the commissioning of residential buildings in the post-Soviet countries

The article is devoted to the description of procedures of economic and mathematical modeling of trends in the field of housing construction taking into account the peculiarities of various countries of the post-Soviet space. The results of analysis of well-known scientific publications on forecasting the dynamics of housing market indicators are presented. It has been shown that most domestic and foreign scientists as the most effective methods of modeling these indicators consider methods of analyzing time trends, in which polynomials of high (in some cases up to the fourth degree) order are used to approximate the available retrospective data. Other common approaches to solving this problem are the use of short-term forecasting based on moving average algorithms, as well as the use of the SARIMA model, which takes into account the trend and seasonal wave. The article shows that these methods do not fully take into account the profound changes in the construction complexes of the post-Soviet states caused by the significant structural transformation of their socio-economic systems. The authors proposed to use econometric models based on regressions with dummy variables to model the main indicators of housing construction, taking into account the complex structure of the external and internal environment of national construction complexes. It has been shown that in a significant number of practical situations, a fairly simple but effective way to take into account the components of the time series of the indicators under consideration in one complex model is to use the model of "change in growth (fall)" when choosing the time of the beginning (end) of a crisis situation as a characteristic point. The results of modeling the main indicators of housing construction for various countries of the post-Soviet space showed that the proposed model when constructing the medium-term forecast allows taking into account the situation component of the analyzed time series. Read more...

Using econometric models to forecast fixed asset investments

One of the key factors in the country’s GDP growth is reproducible capital, which lays the foundation for the production of products, works and services. Accordingly, the study of the state, structure and dynamics of the dominant component, fixed assets, is one of the priority tasks of statistics and econometrics. This implies the purpose of the study, which is to assess the predictive capabilities of econometric models. To achieve this goal, a pool of mathematical-statistical and econometric methods was used, in particular tabular and graphic, descriptive statistics, correlation-regression, adaptive modeling. The main results include: analysis of the structure of investments did not find new or hidden patterns, so investments are directed to the modernization or renewal of capital-intensive areas – these are buildings, structures and land (about 40% of the total investment), the main industries are industry and transport; visual analysis of the dynamics of the temporary series of investments in fixed assets showed the presence of a long-term, seasonal and situational component; the construction of 6 econometric models reflecting the complex dynamics of the macro indicator in question made it possible to distinguish two adaptive models belonging to the group; thus, the best forecast opportunities for complex dynamics of investments in Russian fixed assets are observed in the three-parameter exponential smoothing model and SARIMA (1,0,0)(1,1,0) [4]. The results obtained in the course of the study will be useful for scientists involved in modeling and predicting complex-structured time series. Read more...

Analysis of instrumental methods for modeling stochastic processes in the economy

In this paper, the authors conduct a comparative analysis of instrumental methods used in modeling stochastic processes, namely, component analysis of time series, fractal modeling and modeling using p-adic mathematics. As an object of study, the authors chose the dynamics of the MICEX index. At the first step of the work, the authors carry out a detailed component analysis of the time series, which made it possible to identify the main development trend in the form of a quadratic function; periodic fluctuations with a period of six levels and a cyclical component describing fluctuations in the world economy with a period of fifty-five levels. At the second step of the work, the authors simulate the dynamics of the MICEX index using a fractal theory based on the self-similarity of the development of the economic process, which showed the ergodicity of the series under study with a stable influence of only the last twenty-four levels. The third step of the work was the p-adic modeling of the patterns existing in the series under study, which allowed the authors to reduce the model error to 6.8%. As a result of the work, a forecast of the dynamics of the MICEX exchange rate at four levels is presented, presented in three scenarios: optimistic, realistic and pessimistic. As conclusions of the work, an analysis was made of the possibility of using the considered methods for multiple, medium and long-term forecasts; the complexity of the methods and the need to use special software products are evaluated. Read more...

Analysis and testing of neural network TCP/IP packet routing algorithms in private virtual tunnels

One of the most important components of the global Internet are traffic control and management systems. In order to achieve uninterrupted information and communication interaction, the organization of the process is constantly changing, covering not only individual subnets, but also p2p network architectures. The dominant areas for improving the network structure include 5G, IoT and SDN technologies, but their implementation in practice leaves the issue of ensuring the information security of networks built on their basis without a satisfactory solution. Current virtual tunnel deployment topologies and intelligent traffic distribution components provide only partial solutions, particularly in the form of access control based on user traffic and security through dedicated user certificates. The deployment of a tunnel is of particular importance in cases where it is necessary to ensure consistency and coordination of the work of complex socio-economic systems, an example of which is the information and communication exchange between participants in scientific and industrial clusters formed to implement projects for the creation of innovative products. However, existing solutions have disadvantages such as the need to purchase a license for full-featured access to the software product and specialized configuration of client-server authentication that provides secure access to a remote network route. The approach proposed by the authors, based on neural network distribution of traffic between clients of a private dedicated network, allows us to eliminate the noted shortcomings. Based on this principle, a multi-module system for intelligent packet routing was created and tested through unit testing. An analysis of the effectiveness of using a trained network address distribution model is presented in comparison with the use of a DHCP server based on the isc-dhcp-server package, distributed as the dhcpd service. Read more...

Using swarm intelligence algorithms to determine the composition of a multi-project

One of the promising ways to reduce the dependence of domestic industry on the supply of critical goods, components and raw materials necessary for the construction and effective functioning of multi-stage production and technological chains is to intensify processes for their import substitution, including through various measures of state support. However, the critical need for a wide range of products requires the selection of the most “promising” projects for inclusion in program-target documents using a set of criteria (sometimes even non-financial). As a result, there arises an urgent scientific and practical task of developing approaches to the formation of multi-projects (a set of projects) that can qualify for state support under various programs to reduce import dependence and overcome the technological backwardness of Russian industry, based on the use of modern economic and mathematical methods. In the application to this task, a multi-project can be represented as a “set” (unrelated projects), a “chain” (rigid sequence of projects) or a “network” (projects with complex logical-temporal interrelations). The specifics of each type determine the conditions and impose restrictions on the processes of selecting components for inclusion in their composition, which consists of finding the best combination of projects and/or programs, i. e., it is reduced to a task of conditional multidimensional optimization. In the absence of a requirement to find a “strictly optimal” composition, one can use metaheuristic methods that are capable of finding solutions close to these in an “acceptable” time. Among them, the largest and most well-known class are swarm intelligence algorithms based on the principles of collective behavior of a population of living organisms. To form the composition of multi-projects, the article proposes to use algorithms inspired by the collective behavior of a pack of wolves (Grey Wolf Optimizer) and a school of fish (Fish School Search) to satisfy their food needs. To increase the efficiency of their use for solving the task of finding the best composition of a “set” and “network” of projects, their hybridization with fuzzy logic methods (in particular, fuzzy clustering and fuzzy-logical inference) was proposed. Read more...