Modeling and Forecasting of Russian Federation Cheese Production and Total Cheese Used

Authors

  • Mostafa Abotaleb Department of System Programming, South Ural State University, Chelyabinsk, Russia
  • Ammar Kadi Research Scholar, Department of Food and Biotechnologies, South Ural State University, Chelyabinsk, Russia
  • Irina Potoroko Department of Food and Biotechnologies, South Ural State University, Chelyabinsk, Russia
  • Priyanka Lal Department of Agricultural Economics and Extension, Lovely Professional University, Punjab, India
  • Soumik Ray Centurion University of Technology and Management, Paralakhemundi, Odisha, India
  • Deepa Rawat College of Forestry, VCSG Uttarakhand University of Horticulture and forestry, Ranichauri, Tehri Garhwal, Uttarakhand, India
  • Tufleuddin Biswas Department of Agricultural Economics and Statistics, MS Swaminathan School of Agriculture, Centurion University of Technology and Management, Khurda, Odisha, India
  • Pankaj Kumar Singh Department of Agricultural Economics and Statistics, MS Swaminathan School of Agriculture, Centurion University of Technology and Management, Khurda, Odisha, India
  • Pradeep Mishra College of Agriculture, Rewa, Jawaharlal Nehru Krishi Vishwavidyalaya, Jabalpur, India
  • Shikha Yadav Department of Geography, Miranda House, University of Delhi, New Delhi, India

Keywords:

Box Cox transformation, time series analysis, Holt’s model, TBATS model, prediction

Abstract

The primary goal of this research was to evaluate the forecasted behavior of cheese production and total uses in Russia from 1988 to 2020. As a result of a supply-demand imbalance, cheese imports from other nations were necessary to close the gap. Before creating the model, the training and testing sets were split. For both data series, the linear trend model developed by TBATS and Holt was utilized to create the model and estimate the projection. For both sigma and AIC, the best prediction model was found in the TBATS model. Because the TBATS model can decompose data series, we found it to be the best prediction model over Holt’s model. As a result of its poorer goodness of fit in both data series, Holt’s linear trend model was the best model to use. This study has proven itself to be a valuable resource for policymakers, stakeholders, and researchers alike. Furthermore, we anticipate that the findings of this study will serve as a catalyst for the development of an advanced statistical model or machine learning model for cheese production in the future.

References

Abotaleb MSA. Predicting COVID-19 cases using some statistical models: An application to the cases reported in China Italy and USA. Acad J Appl Math Sci. 2020; 6(4): 32-40.

Abotaleb M, Ray S, Mishra P, Karakaya K, Shoko C, Al Khatib AMG, Ray M, Fernando WHH, Lounis M, Balloo R. Modelling and forecasting of rice production in south Asian countries. AMA Agric Mech Asia Afr Lat Am. 2021; 51(3): 1611-1627.

Badr A, Makarovskikh T, Mishra P, Abotaleb M, Al Khatib AMG, Karakaya K, Redjala S, Dubey A, Attal E. Modelling and forecasting of web traffic using Holt’s linear, bats and TBATS models. J Math Comput Sci. 2021; 11(4): 3887-3915.

Borodin K. The model of differentiated products market for its development forecasting (on the example of the cheese market). Econ Math Methods. 2020; 56(3): 68-78.

De Livera AM, Hyndman RJ, Snyder RD. Forecasting time series with complex seasonal patterns using exponential smoothing. J Am Stat Assoc. 2011; 106(496): 1513-1527.

De Livera AM. Automatic forecasting with a modified exponential smoothing state space framework.

Monash Econometrics and Business Statistics Working Papers. 2010; 10(10): 1-29.

Devi M, Kumar J, Malik DP, Mishra P. Forecasting of wheat production in Haryana using hybrid time series model. J Agric Food Res. 2021; 5:100175.

Durbin J, Koopman SJ. Time series analysis by state space methods. 2nd ed. New York: Oxford University Press; 2012.

Hannan EJ, Terrell RD, Tuckwell NE. The seasonal adjustment of economic time series. Int Econ Rev. 1970; 11(1): 24-52.

Harvey AC. Forecasting, structural time series models and the Kalman filter. New York: Cambridge University Press; 1990.

Holt CC. Forecasting seasonals and trends by exponentially weighted moving averages. Int J Forecasting. 2004; 20(1): 5-10.

Kuzin AA, Medvedeva NYA, Zadumkin KA, Vakhrusheva VV. Development scenarios for Russia's dairy industry. Econ Agro-Ind Complex. 2018; 11(6): 73-88.

Kuzina E Yu, Ostretsov, VN. The state and prospects of cheese production in Russia. Dairy Bull. 2016; 1: 21.

Lama A, Singh KN, Singh H, Shekhawat R, Mishra P, Gurung B. Forecasting monthly rainfall of Sub-Himalayan region of India using parametric and non-parametric modelling approaches. Model Earth Syst Environ. 2022; 8:837-845.

Matalas A, Zampelas A, Stavrinos V. The Mediterranean diet: Constituents and health promotion. Boca Raton: CRC Press; 2001.

Mishra P, Fatih C, Niranjan HK, Tiwari S, Devi M, Dubey A. Modelling and forecasting of milk production in Chhattisgarh and India. Indian J Anim Res. 2020; 54(7): 912-917.

Mishra P, Ray S, Al Khatib AMG, Abotaleb M, Tiwari S, Badr A, Balloo R. Estimation of fish production in India using ARIMA, Holt’s linear, BATS and TBATS models. Indian J Ecol. 2021a; 48(5): 1254-1261.

Mishra P, Al Khatib AMG, Sardar I, Mohammed J, Karakaya K, Dash A, Ray M, Narsimhaiah L, Dubey A. Modeling and forecasting of sugarcane production in India. Sugar Tech. 2021b; 23, 1317-1324.

Mishra P, Matuka A, Abotaleb MSA, Weerasinghe WPMCN, Karakaya K, Das SS. Modeling and forecasting of milk production in the SAARC countries and China. Mod Earth Syst Environ. 2022; 8: 947-959.

Murtuzalieva TV, Panasenko SV, Slepenkova EV, Tultaev TA, Pogorilyak BI. Import substitution strategy and ways of marketing, its implementation using the dairy industry of the Russian Federation as an example. Acad Strateg Manag J. 2017; 16(2): 1-14.

Ray S, Bhattacharyya B. Time series modeling and forecasting on pulses production behavior of India. Indian J Ecol. 2020;47 (4): 1140-1149.

Ray S, Das SS, Mishra P, Al Khatib AMG. Time series SARIMA modelling and forecasting of monthly rainfall and temperature in the south Asian countries. Earth Syst Environ. 2021; 5(3): 531-546.

Svetunkov I, Kourentzes N, Ord JK. Complex exponential smoothing. Nav Res Logist. 2022; 69(8): 1108-1123.

Taylor JW. Exponential smoothing with a damped multiplicative trend. Int J Forecast. 2003; 19(4): 715-725.

Downloads

Published

2024-09-28

How to Cite

Abotaleb, M. ., Kadi, A. ., Potoroko, I. ., Lal, P. ., Ray , S., Rawat, D. ., Biswas, T. ., Kumar Singh, P. ., Mishra, P. ., & Yadav, S. . (2024). Modeling and Forecasting of Russian Federation Cheese Production and Total Cheese Used. Thailand Statistician, 22(2), 894–908. Retrieved from https://ph02.tci-thaijo.org/index.php/thaistat/article/view/256077

Issue

Vol. 22 No. 2 (2024): April

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.