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.

本文介绍了适用于时间序列动态估计的复杂方法操作的结果，其特点是联合使用经典和新的“非线性”统计。作者提出和测试的方法以预估计和估计模型的形式呈现，用于评估伏尔加格勒地区（1930-2019 年）粮食产量时间序列趋势稳定性并获得预测。测试了以下非线性动力学方法：Hurst 归一化范围法、相平面分析和线性元胞自动机。对实际产量数据的分析和预测结果以农业经济系统建模下层值的形式呈现，而下层值又是上层——农业经济系统管理层模型的输入数据。