APPROACHES AND INFORMATION COMPONENT OF ECONOMIC FORECASTING USING ARTIFICIAL NEURAL NETWORKS

DOI 10.32651/262-67

Issue № 2, 2026, article № 9, pages 67-73

Section: DIGITALIZATION IN THE AGRO-INDUSTRIAL COMPLEX

Language: Russian

Original language title: ПОДХОДЫ И ИНФОРМАЦИОННАЯ СОСТАВЛЯЮЩАЯ ЭКОНОМИЧЕСКОГО ПРОГНОЗИРОВАНИЯ С ИСПОЛЬЗОВАНИЕМ ИСКУССТВЕННЫХ НЕЙРОННЫХ СЕТЕЙ

Keywords: AGRI-FOOD SYSTEMS, AGRICULTURE, AGRIBUSINESS ENTITIES, METHODOLOGICAL APPROACHES, FORECASTING METHODS, MACHINE LEARNING, ARTIFICIAL NEURAL NETWORKS

Abstract: The purpose of this article is to systematize methodological approaches to economic forecasting using artificial neural networks. The empirical basis of the research was made up of the works of domestic and foreign researchers in the field of forecasting the economy of agriculture and agriculture, as well as the personal experience of the authors. Based on the generalization of interdisciplinary literature, the article provides a comparative analysis of methodological approaches to building economic forecasts using artificial neural networks, their advantages and disadvantages are considered. The target orientation, advantages, discussion aspects and the relationship between individual approaches were identified, which allowed for their systematization. In relation to economic entities, the approaches were classified as methods of spatial and dynamic forecasting. A conceptual view on the formation of an information base is substantiated, taking into account the specifics of individual approaches. The obtained research results contribute to the understanding of economic work in agribusiness entities in terms of forecasting activities, contribute to building an optimal structure for obtaining reliable information and timely detection of market anomalies.

Authors: Dubovitskii Aleksandr Alekseevich, Klimentova Elvira Anatolevna, Babkina Ekaterina Sergeevna