Forecasting of Prices for Agricultural Products on the Basis of Fractal-Integrated ARFIMA Model

  • L. A. ALEKSANDROVA Saratov State Vavilov Agrarian University, Russian Federation
  • I. P. GLEBOV Saratov State Vavilov Agrarian University, Russian Federation
  • Y. V. MELNIKOVA Saratov State Vavilov Agrarian University, Russian Federation
  • I. N. MERKULOVA Saratov State Vavilov Agrarian University, Russian Federation


The article is devoted to the analysis and forecasting of time series by methods of nonparametric statistics. The authors present the results of the analysis of time series of weekly prices for sunflower in Russia in2008-2015 by the Hurst’s method of rescaled range (R/S-analysis). The result of this research was to define the properties of nonlinearity of the specified time series and to reveal its fractal characteristics. The results obtained were used in forecasting of price situation in the market of sunflower seeds based on fractal-integrated ARFIMA (p,d,q) model for the short term prospects. The proposed approach to price forecasting can be used by agricultural producers in planning of their activity. Price situation forecast in this case will serve as a guide in sales planning, the essence of which is providing the maximum amount of revenue at the optimum volume of production and the achieved level of production costs. Using reasonable price forecast will enable farms producing sunflower not only to profit from competent production-marketing policy based on the price situation forecast of the market but also to maintain a high level of competitiveness and to sustain continuous activity in the given interval, to make scheduled payments to the budgets of different levels and to make innovations. Thus, planning of production indicators and agricultural products sales based on market situation forecasts for different crops will promote to the rise in the efficiency of crop production.


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How to Cite
ALEKSANDROVA, L. A. et al. Forecasting of Prices for Agricultural Products on the Basis of Fractal-Integrated ARFIMA Model. Journal of Advanced Research in Law and Economics, [S.l.], v. 8, n. 6, p. 1693-1700, sep. 2017. ISSN 2068-696X. Available at: <>. Date accessed: 25 feb. 2024.