Neural Artificial Networks as an Effective Tool for Adaptive Forecasting in the Agrarian Sector of the Economy

The purpose of the article is to provide scientific and methodological substantiation and development of a model for predicting the development of agricultural production in the Kirovohrad region on the basis of the application of artificial neural networks. The peculiarity of forecasting at the regional level is the need to take into account a significant number of exogenous and endogenous factors of influence. For prediction along with traditional methods of econometric analysis of time series it is expedient to use artificial neural networks. Correlation dependence of factors of development of the agrarian sector of the economy influencing the dynamics of gross agricultural production is determined. The theoretical model of forecasting of gross agricultural production is developed. According to research results, using the method of artificial neural networks and the software product of the analytical platform of the Deductor Academic 5.3.0.88 package, a methodical approach has been developed to construct a forecasting model for gross agricultural production. The essence of the proposed approach is based on a combination of methods of adaptive forecasting and the instrument of artificial neural networks. The methodical approach of adaptive forecasting of gross output of agricultural production is developed. The model of adaptive prediction based on artificial neural networks allows to take into account a significant number of factors of influence and tendencies in the development of ultra-complicated systems, which include agriculture, as well as to provide a lower error margin of forecast.