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

Abstract

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.

Total Health Expenditure and Its Driving Factors in China: A Gray Theory Analysis

“…However, many scholars have noted some flaws in the existing GM(1,N) model’s prediction ability [ 38 , 39 , 40 ]. Zeng et al [ 41 ], experts in gray prediction theory, pointed out three major defects in the traditional multivariate gray prediction model GM(1,N), that is, the mechanism’s defects caused by the over-idealization of the derivation process, the parameter’s defects caused by the “nonhomology” of parameter estimation and the application object, and the structural defects of lack of data mining and equivalent substitution. These are all important issues that affect the accuracy of the prediction model.…”

An Improved Nonlinear Grey Bernoulli Model Based on the Whale Optimization Algorithm and Its Application

“…e ONGBM (1, 1) model has the following prediction performance with a relatively lower MAPE value of 0.28%. As mentioned in [44], as a proper forecasting method, it performs excellently in simulation and should do well in the prediction stage. By observing Table 8, it is easy to find that the proposed model is better than other grey models again because of its lower RMSE value of 0.40 and MAPE value of 1.72%.…”

An Estimation of Digital Banking Practices in Turkish Banks on Recruitments Under Human Resources Strategies

“…Ortalama göreli hata ise eşitlik 12 ile hesaplanır. , maliyet tahmini (Özer Keçe, Ömürbek, Acar, 2016), kredi kartı kullanımı (Yıldırım, Keskintürk, 2015), bankalardaki bireysel kredi risklerinin tahmini (Aksoy ve Akçakanat, 2019), tekstil moda renk tercih trendi (Lin vd., 2010), sığır eti tüketimi (Zeng, Li, Meng ve Zhang, 2019), bilimsel yayınların tahmini (Aydemir, Bedir ve Özdemir, 2013), basınç verileri (Kaleli, Ceviz ve Erentürk, 2014), deformasyon tahmini (Taşçı, 2017), doğalgaz talep tahmini (Oruç ve Çelik Eroğlu, 2017;Wang, Liu ve Yang, 2018;Lu, 2018), ekonomik büyüme tahmini (Önalan ve Başeğmez, 2018), elektrik üretim ve tüketim tahmini (Zhao ve Zhou, 2018;Şahin, 2018;Es, 2020), sağlık sektörü harcamaları ( Öztürk ve Bilgil, 2019), sağlık sektörüne talep tahminleri (Zor ve Çebi, 2018;Şahin, 2019;Oruç ve Başağaoğlu Fındık, 2020), kripto para fiyatları tahmini (Şahin ve Bağcı, 2020), inşaat endüstrisi tedarik zinciri tahmini (Nguyen ve Tran, 2018;Nguyen, 2020), turist sayıları tahmini (Javed, Ikram, Tao ve Liu, 2020), kalite yönetm sistemlerinin geleceği (Ikram, Zang ve Sroufe, 2020), uçak endüstrisi tahmini (Carmona-Benitez ve Nieto, 2020), otomobil endüstrisi talebi (Tran, 2018) gibi farklı disiplinlere ait konular üzerinde tahminlemeler yapıldığı görülmüştür. Gri Tahmin yöntemlerinden birisi olan GM(1,1) Modeli kullanılarak bankalara ait bazı değişkenlerinin tahminlenmesi bu çalışmanın özgün tarafına vurgu yapmaktadır.…”