The work analyzes existing approaches to forecasting contract execution, including traditional statistical models and modern methods based on machine learning. A comparative analysis of various machine learning algorithms, such as logistic regression, decision trees, random forest and neural networks, was carried out to identify the most effective forecasting models.An extensive database of information on government contracts was used as initial data, including information about contractors, contract terms, deadlines and other significant factors. A prototype of an intelligent forecasting system was developed, testing was carried out on real data, as well as an assessment of the accuracy and reliability of the resulting forecasts. The results of the study show that the use of machine learning methods can significantly improve the quality of forecasting the execution of government contracts compared to traditional approaches

Keywords: intelligent system, mathematical modeling, government procurement, government contracts, software package, forecasting, machine learning

The article proposes the use of intelligent methods for predicting the reliability of contract execution as a key element of the system for ensuring information security of the critical infrastructure of financial sector organizations. Based on the analysis of historical data and the use of machine learning methods, a comprehensive model for assessing and predicting the risks of failure or poor performance of contracts by suppliers has been developed. It is shown how the use of predictive analytics can improve the efficiency of information security risk management, optimize planning and resource allocation, and make informed decisions when interacting with suppliers of critical services and equipment.

Keywords: intelligent system, predictive analytics, information security, critical infrastructure, financial sector, contract execution, machine learning

The paper presents an ordinary and dynamic piecewise linear Leontief model with nonlinear predictors for the gross domestic product of Russia. The following variables are used as independent variables: the number of able-bodied population, retail trade turnover, and capital investment. In the right part of the model, along with the current value of investment volume, its lagged values with a lag of one and two years are included. The average relative error of approximation of each model and the values of the vector of triggers of independent variables were calculated.

Keywords: Leontief model, nonlinear predictors, gross domestic product, least modulus method, average relative approximation error, vector of triggers, linear-Boolean programming problem

In this paper, we investigate the possibility of applying the theory of Monty Hall's paradox in tasks that require the need for an optimal choice of a strategy for developing the innovative potential of an enterprise. The article provides recommendations for taking into account and constructive use of the effects that affect the involved experts, in particular, the Condorcet principle and paradox. The paper explores the limits of applicability of the Monty Hall paradox theory. Its applicability is determined, together with considerations about the profitability of changing the initial choice in problems with the so-called "random intelligence".

Keywords: decision support systems, mathematical modeling, expert evaluation, Monty Hall's paradox, project management, collective assessment, Condorcet's paradox, enterprise management, assessment of enterprise characteristics, innovative potential of an enterpris

The article discusses commodity futures price forecasting using a temporal convolutional network. Commodity futures forecasting is an important task for investors and traders because it allows you to predict future prices and the direction of the market. Commodity futures forecasting can be done using a variety of methods and approaches. One such approach is the use of deep learning models, which consists in predicting futures quotes using artificial neural networks. There are many types of neural networks, among them the most popular for the task of processing time series are recurrent neural networks. However, recurrent neural networks have certain disadvantages that a temporal convolutional network does not have. The temporal convolutional network architecture has unique features such as parallel processing of data, extraction of short- and long-term dependencies, and extraction of important features on different time scales. An experiment was conducted to assess the accuracy of predicting the closing price of seven commodity futures using a temporary convolutional network and an ARIMA statistical model with automatic selection of parameters. As a result of the experiment, it was revealed that the temporary convolutional network is superior to the statistical ARIMA model and is a very effective model for forecasting commodity futures. However, despite the high potential of the proposed forecasting model, it is also important to take into account various other analytical methods, such as fundamental analysis and expert opinion.

Keywords: machine learning, temporal convolutional neural network, commodity futures forecasting, commodities, financial time series

In order to provide information support for decision-making on the issuance of bank guarantees for the execution of a contract in the field of public procurement, it is important for banks to obtain historically accumulated information on the execution of government contracts. This is necessary to assess the possibility of the supplier's performance of his future contract. This can be done by collecting and aggregating information about contracts from the Unified Information System in the field of procurement. The paper proposes to use IT technologies and data analysis to predict the performance of the contract and identify bona fide suppliers. In the work, a selection of primary data on contracts was formed for modeling using the parsing of the FTP server of the Unified Information System in the field of procurement, and the parsed data was preprocessed for use in machine learning models.

Keywords: information system, data analysis, government contract, data parsing, machine learning

The authors of the article have developed an economic and mathematical model for determining the influence of factors characterizing the specifics of dismantling works on the operating costs and duration of operation of machines and mechanisms, the scientific novelty of which is that, unlike existing models, it takes into account the normative-parametric method, regression and covariance dependencies, which makes it possible to assess the totality of the influence of factors on operational costs and duration of operation of machines and mechanisms, describe the procedure for calculating the operating costs and duration of operation of machines and mechanisms and generate the amount of investment in the operating costs of machines and mechanisms.

Keywords: dismantling works, economic and mathematical model, operating costs, machines and mechanisms

The behavior of the participants in the production process at the enterprise is modeled in cases of two-level and three-level hierarchy in the conditions of corruption, checking products for quality and punishing players in a number of cases. The formulas for the interaction of players and their winning strategies are given. A number of functions are standard formulas. The Stackelberg equilibrium was obtained programmatically for a two-level system in statics, for a three-level system in dynamics. The proposed formulation is based on the theories of G.A. Ugolnitsky and A.B. Usov. The results obtained allow us to identify shortcomings in a number of enterprises, as well as in theory, and continue its development.

Keywords: analytic-geometric analysis, simulation modeling, Stackelberg equilibrium, hierarchical system, game-theoretic modeling, corruption in organizations

Despite the existing differentiation of the regions of the Russian Federation in terms of natural and climatic conditions and the level of socio-economic development, the main principle of the state cadastral valuation is aimed at the uniformity of its implementation. The study in this paper is aimed at revealing the concept of "regional features" as a factor leading to a discrepancy in the methodology for determining the cadastral value of real estate at the regional level. The article highlights the factors of regional specifics that form the features of the appraisal work to determine the cadastral value of land plots in 2022 on the territory of St. Petersburg and the Perm Territory, as well as recommendations for improving the existing unified methodology.

Keywords: state cadastral valuation, mass valuation, taxation, cadastral value, market value, differentiation of regions, regional peculiarity, real estate object, land plot

To analyze such significant indicator of the locomotive industry as the value of import customs payments, the dependence of the volume of import customs payments on weight, cost, weighted average rate and dollar exchange rate is examined. Mathematical modeling was carried out on the basis of monthly data during the period from 2019 up to 2021 of the volumes of customs payments and their parameters of the Group 86 of the Customs nomenclature of EAEU FEA "Railway locomotives or tram motor cars, rolling stock and their parts; track equipment and devices for railways or tram tracks and their parts; mechanical (including electromechanical) signaling equipment of all types". The interrelations of these variables are represented by a system of simultaneous structural equations. The import substitution trend developing in Russia implies strengthening the development of national industries and, accordingly, pursuing a customs policy that implies support for domestic producers and reduction of import supplies. Thus, it is relevant to determine the impact of various factors on the amount of customs duties levied by the customs authorities on foreign economic activity (FEA) participants in operations involving the import of spare parts and other goods for the locomotive building industry to make the right strategic decisions concerning customs restrictions on imports of goods of this industry.

Keywords: locomotive building industry, mathematical modeling, linear multiple regression, multicollinearity, system of simultaneous equations, identification, structural form of the model, reduced form of the model