An improved gray prediction model for China’s beef consumption forecasting

To balance the supply and demand in China's beef market, beef consumption must be scientifically and effectively forecasted. Beef consumption is affected by many factors and is characterized by gray uncertainty. Therefore, gray theory can be used to forecast the beef consumption, In this paper, the structural defects and unreasonable parameter design of the traditional gray model are analyzed. Then, a new gray model termed, EGM(1,1,r), is built, and the modeling conditions and error checking methods of EGM(1,1,r) are studied. Then, EGM(1,1,r) is used to simulate and forecast China’s beef consumption. The results show that both the simulation and prediction precisions of the new model are better than those of other gray models. Finally, the new model is used to forecast China’s beef consumption for the period from 2019–2025. The findings will serve as an important reference for the Chinese government in formulating policies to ensure the balance between the supply and demand for Chinese beef.

[1]  Bo Zeng,et al.  A new multivariable grey prediction model with structure compatibility , 2019, Applied Mathematical Modelling.

[2]  Ricardo Téllez Delgado,et al.  Factors affecting beef consumption in the valley of Mexico , 2015 .

[3]  A Curk Bovine spongiform encephalopathy crisis in Europe and its impact on beef consumption in Slovenia. , 1999, Revue scientifique et technique.

[4]  Ozgur Kisi,et al.  Modeling of air pollutants using least square support vector regression, multivariate adaptive regression spline, and M5 model tree models , 2017, Air Quality, Atmosphere & Health.

[5]  Dimitris Korobilis,et al.  Quantile regression forecasts of inflation under model uncertainty , 2017 .

[6]  António Rua,et al.  A wavelet-based multivariate multiscale approach for forecasting , 2017 .

[7]  P. Oguntunde,et al.  Relationship between rice yield and climate variables in southwest Nigeria using multiple linear regression and support vector machine analysis , 2018, International Journal of Biometeorology.

[8]  Kulwinder Singh Parmar,et al.  Statistical, time series, and fractal analysis of full stretch of river Yamuna (India) for water quality management , 2014, Environmental Science and Pollution Research.

[9]  Rahmat-Allah Hooshmand,et al.  Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm , 2014 .

[10]  Huan Guo,et al.  The modeling mechanism, extension and optimization of grey GM (1, 1) model , 2014 .

[11]  Morten Birkved,et al.  Potential to curb the environmental burdens of American beef consumption using a novel plant-based beef substitute , 2017, PloS one.

[12]  C. J. Franco,et al.  Research in Financial Time Series Forecasting with SVM: Contributions from Literature , 2017, IEEE Latin America Transactions.

[13]  Sifeng Liu,et al.  A self‐adaptive intelligence gray prediction model with the optimal fractional order accumulating operator and its application , 2017 .

[14]  Kulwinder Singh Parmar,et al.  Modeling of air pollution in residential and industrial sites by integrating statistical and Daubechies wavelet (level 5) analysis , 2017, Modeling Earth Systems and Environment.

[15]  Tambi Ne,et al.  Patterns of change in beef production and consumption in Africa. , 2003 .

[16]  David M. Broday,et al.  Long-term aerosol climatology over Indo-Gangetic Plain: Trend, prediction and potential source fields , 2018 .

[17]  Chuan Li,et al.  Forecasting the natural gas demand in China using a self-adapting intelligent grey model , 2016 .

[18]  Jianzhou Wang,et al.  Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China , 2012 .

[19]  Kulwinder Singh Parmar,et al.  Statistical variability comparison in MODIS and AERONET derived aerosol optical depth over Indo-Gangetic Plains using time series modeling. , 2016, The Science of the total environment.

[20]  N E Tambi,et al.  Patterns of change in beef production and consumption in Africa. , 2003, Revue scientifique et technique.

[21]  Jeffrey Forrest,et al.  Grey Data Analysis - Methods, Models and Applications , 2017, Computational Risk Management.

[22]  Rob J. Hyndman,et al.  Crude oil price forecasting based on internet concern using an extreme learning machine , 2018, International Journal of Forecasting.

[23]  Wiktor L. Adamowicz,et al.  Habit, BSE, and the Dynamics of Beef Consumption , 2011 .

[24]  Júlio Otávio Jardim Barcellos,et al.  Conceptual model to identify factors with influence in Brazilian beef consumption , 2015 .

[25]  Kulwinder Singh Parmar,et al.  River Water Prediction Modeling Using Neural Networks, Fuzzy and Wavelet Coupled Model , 2014, Water Resources Management.

[26]  Sagar Maji,et al.  Comparison of ARIMA and ANN approaches in time-series predictions of traffic noise , 2016 .

[27]  H. J. Lu,et al.  An improved neural network-based approach for short-term wind speed and power forecast , 2017 .

[28]  Christian A. Klöckner,et al.  A stage model as an analysis framework for studying voluntary change in food choices – The case of beef consumption reduction in Norway , 2017, Appetite.

[29]  Wiktor L. Adamowicz,et al.  BSE and the Dynamics of Beef Consumption: Influences of Habit and Trust , 2009 .

[30]  Xin Ma,et al.  A novel fractional time delayed grey model with Grey Wolf Optimizer and its applications in forecasting the natural gas and coal consumption in Chongqing China , 2019, Energy.

[31]  Bo Zeng,et al.  Improved multi-variable grey forecasting model with a dynamic background-value coefficient and its application , 2018, Comput. Ind. Eng..

[32]  V. Fulgoni,et al.  Contribution of beef consumption to nutrient intake, diet quality, and food patterns in the diets of the US population. , 2012, Meat science.

[33]  O. Kisi,et al.  Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution , 2016 .

[34]  Abinet Tesfaye Eseye,et al.  Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information , 2018 .

[35]  Kulwinder Singh Parmar,et al.  Time series model prediction and trend variability of aerosol optical depth over coal mines in India , 2015, Environmental Science and Pollution Research.

[36]  V. Vlasenko,et al.  Implicit operator differential equations and applications to electrodynamics , 2000 .

[37]  Chen-Fang Tsai,et al.  Dynamic grey platform for efficient forecasting management , 2015, J. Comput. Syst. Sci..

[38]  Kulwinder Singh Parmar,et al.  Statistical analysis of aerosols over the Gangetic–Himalayan region using ARIMA model based on long-term MODIS observations , 2014 .

[39]  Wenqing Wu,et al.  The conformable fractional grey system model. , 2018, ISA transactions.

[40]  Coşkun Hamzaçebi,et al.  Forecasting the annual electricity consumption of Turkey using an optimized grey model , 2014 .

[41]  Hamza Erdoğdu,et al.  Modelling beef consumption in Turkey: the ARDL/bounds test approach , 2017 .

[42]  Bo Zeng,et al.  Forecasting the output of shale gas in China using an unbiased grey model and weakening buffer operator , 2018 .