Solving an Uncertainty Model Using Leading Data Synthesis

This article proposes a theoretical solution to the problem of overcoming variable uncertainty in leading indicator calculations using economic data expected by business communities as an example. The novelty of the proposed approach lies in its ability to fill a gap in the technology for processing primary data on business community opinions, which is essential for maximizing the utilization of information relevant for decision making. T he objective of this article is to present the results of solving a model for encapsulating and decapsulating information with uncertain outcomes. The research method was to construct nonlinear paired regression equations for time series of economic, statistical, and sociological information. The conditions of the model with time series of an independent uncertain variable are examined, and verification and quality assessment of the model are discussed. The study was conducted from 1993 to 2025 using the Bank of Russia and the National Research Financial Institute (NIFI) databases. The data sources included the Moscow Interbank Currency Exchange, Investing, and the analytical departments of commercial banks and brokerage firms. The model was built on a continuous sample of forecast data and opinions from participants in the derivatives markets. T he conclusion presents the key results of the model solution, which include a significant increase (by 40%) in the classification accuracy testing for machine learning of the neural network for searching and preprocessing exchange trading data. The advantages of solving a multiple paired regression equation model using a time series of economic indicator values expected by business communities, including a de-encapsulated uncertain variable, are discussed relative to standard solutions of paired regression equations.