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Analysis of the Forecast Price as a Factor of Sustainable Development of Agriculture
Agronomy ( IF 3.3 ) Pub Date : 2021-06-18 , DOI: 10.3390/agronomy11061235
Maxim Tatarintsev , Sergey Korchagin , Petr Nikitin , Rimma Gorokhova , Irina Bystrenina , Denis Serdechnyy

Analysis of the rise in prices for consumer goods is a state’s priority task. The state assumes the obligation to regulate pricing in all spheres of consumption. First of all, the prices for essential commodities to which agricultural products belong are analyzed. The article shows the changes in prices for consumer goods of agricultural products (sugar) during a pandemic. The analysis of forecasting prices for sugar and its impact on the development of its production is carried out. The construction of the forecast model was based on extrapolation. The structure of a forecast model for price changes was based on the analysis of the time series of the Autoregressive Integrated Moving Average (ARIMA) class. This model consists of an autoregressive model and a moving average model. A forecast of the volume of domestic sugar transportation by rail has been completed. The algorithms implemented this model for searching for initial approximations and optimal parameters for the predictive model. The Hirotsugu Akaike Information Criterion (AIC) was used to select the best model. The algorithms were implemented in the Python programming language. The quality check of the description was performed with a predictive model of actual data. An economic interpretation of the rise in sugar prices and proof of the forecast’s truth obtained from a financial point of view were carried out.

中文翻译:

预测价格作为农业可持续发展因素的分析

分析消费品价格上涨是国家的首要任务。国家有义务在所有消费领域规范定价。首先,对农产品所属的基本商品价格进行了分析。文章展示了大流行期间农产品(糖)消费品的价格变化。分析了糖的预测价格及其对生产发展的影响。预测模型的构建基于外推法。价格变化预测模型的结构基于对自回归综合移动平均线 (ARIMA) 类的时间序列的分析。该模型由自回归模型和移动平均模型组成。完成了对国内铁路运输糖量的预测。算法实现了该模型以搜索预测模型的初始近似值和最佳参数。Hirotsugu Akaike 信息准则 (AIC) 用于选择最佳模型。这些算法是用 Python 编程语言实现的。描述的质量检查是使用实际数据的预测模型进行的。对糖价上涨进行了经济解释,并从财务角度证明了预测的真实性。这些算法是用 Python 编程语言实现的。描述的质量检查是使用实际数据的预测模型进行的。对糖价上涨进行了经济解释,并从财务角度证明了预测的真实性。这些算法是用 Python 编程语言实现的。描述的质量检查是使用实际数据的预测模型进行的。对糖价上涨进行了经济解释,并从财务角度证明了预测的真实性。
更新日期:2021-06-18
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