Creating a neural network system for forecasting and managing agricultural production using autocorrelation functions of time series

Abstract

The article considers the features of creating an artificial neural network (ANN) for modelling and forecasting the dynamics of long-term time series (TS) levels of grain yield in arid conditions on the example of the Lower Volga region of the Russian Federation. In order to increase the validity of the choice of architecture and macroparameters developed by ANN, statistical characteristics of the simulated TS were analysed. The autocorrelation function of distribution of levels of long-term series of grain yields is constructed. It is proposed to take into account the characteristics of time lags of autocorrelation functions when selecting ins macroparameters for predicting BP yield. On the basis of preliminary statistical analysis, "peaks" corresponding to the time lags of the autocorrelation function, whose values are determined for different groups of grain crops, are identified. The obtained values are recommended to be taken into account when selecting the value of the time window parameter when constructing neural network models of productivity. This is the basis of the proposed information technology for building ins for predicting crop yields. The results of neural network modelling and yield forecasting can be successfully used for managing agricultural production, including in the arid conditions of the Lower Volga region of the Russian Federation.