Forecasting of agri-food economic systems using artificial neural networks

Abstract. The purpose of the study was to substantiate the applicability of the use of artificial neural networks to the forecasting of agro-food economic systems. Methods. The research is based on the use of elements of the interpretative method in a combination of genetic, structural, functional, complex, systemic, and empirical approaches. The scientific novelty it consists in systematization of algorithms for the implementation of artificial neural networks and substantiation of their applicability for forecasting agro-food economic systems, development of an algorithm and architecture for building a neural network based on multiple data on agricultural markets, substantiation of directions for improving information infrastructure at the firm level. Results. The authors systematized intuitive and formalized forecasting methods, justified the place of methods based on machine learning in this system. The advantages and disadvantages of using artificial neural networks for forecasting agri-food economic systems are considered in detail, the expediency of their use from the point of view of compliance with the principles of forecasting is justified. The analysis of the main types of artificial neural networks allowed us to conclude that the most promising for the implementation of forecasting tasks are competitive neural networks with a back propagation algorithm (LSTM and GRU). The main objectives of building models based on neural networks for use in forecasting economic systems are formulated, the basic provisions of the sequence and methods of deploying neural networks in the forecasting process in the agri-food market are developed, the key elements of the organization of the forecasting process in individual economic entities are considered, practical aspects of the possibility of using a mathematical algorithm for modeling agri-food systems are considered, as well as the conditions for improving the information infrastructure at the firm level in order to ensure the availability of data sources and technologies for their processing.