Development of Neural Network Algorithms for Food Market

Abstract

The current situation is characterized by the restructuring in the world politics and the next waves of anti-Russian sanctions, the lability on the food markets, due to which Russian regions need to monitor closely and ensure food security. The practice of applying neural networks proved to be highly effective for forecasting the market situation, correcting deviating commodity and money flows, analyzing and deriving the most basic information from sociological surveys, formulating expectations on the dynamics of elite politicians' ratings, improving production and service processes, subjecting product quality to comprehensive diagnostics. The present article aims to give a characteristic of food market research by the method of price prediction algorithm, considering as an example agricultural enterprise product analyzed with the comprehensive use of neural network potential. The food market is fully characterized by the conjuncture as an important predictable factor, which is why they study product price forecasting. This article is aimed at showing the principles of price forecasting and the corresponding neural network implementation algorithm generated by the supply of the food market, considering such a type of product as agricultural.