Estimation obtaining instrumental means based on nonlinear dynamics methods

Abstract

This article presents the results of the adapted complex methodology operation for the time series dynamics estimating, its features are in the joint use of both classical and new “nonlinear” statistics. The methods proposed and tested by the author are presented in the form of a pre-estimating and estimating model for assessing the grain yields time series trend stability in the Volgograd region (1930–2019) and obtaining a forecasting. The following methods of nonlinear dynamics were tested: the Hurst normalized range method, phase-plane analysis, and a linear cellular automaton. The results of analysis and forecasting on real yield data are presented in the form of the agro-economic system modeling lower level values, which in turn are input data for models of the upper level - the agro-economic system management level.