Capital University of Economics and Business, Beijing, China.
School of Management Engineering, Capital University of Economics and Business, Beijing, China.
PLoS One. 2021 May 11;16(5):e0251436. doi: 10.1371/journal.pone.0251436. eCollection 2021.
In recent years, the price of pork in China continues to fluctuate at a high level. The forecast of pork price becomes more important. Single prediction models are often used for this work, but they are not accurate enough. This paper proposes a new method based on Census X12-GM(1,1) combination model.
Monthly pork price data from January 2014 to December 2020 were obtained from the State Statistics Bureau(Mainland China). Census X12 model was adopted to get the long-term trend factor, business cycle change factor and seasonal factor of pork price data before September 2020. GM (1,1) model was used to fit and predict the long-term trend factor and business cycle change factor. The fitting and forecasting values of GM(1,1) were multiplied by the seasonal factor and empirical seasonal factor individually to obtain the fitting and forecasting values of the original monthly pork price series.
The expression of GM(1,1) model for fitting and forecasting long-term trend factor and and business cycle change factor was X(1)(k) = -1704.80e-0.022(k-1) + 1742.36. Empirical seasonal factor of predicted values was 1.002 Using Census X12-GM(1,1) method, the final forecast values of pork price from July 2020 to December 2020 were 34.75, 33.98, 33.23, 32.50, 31.78 and 31.08 respectively. Compared with ARIMA, GM(1,1) and Holt-Winters models, Root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE) of Census X12-GM(1,1) method was the lowest on forecasting part.
Compared with other single model, Census X12-GM(1,1) method has better prediction accuracy for monthly pork price series. The monthly pork price predicted by Census X12-GM(1,1) method can be used as an important reference for stakeholders.
近年来，中国猪肉价格持续高位波动，猪肉价格预测显得尤为重要。目前的猪肉价格预测工作多采用单一预测模型，预测精度不够理想。本研究提出了一种基于 Census X12-GM(1,1)组合模型的新方法。
从国家统计局获取 2014 年 1 月至 2020 年 12 月的猪肉月度价格数据。采用 Census X12 模型获取 2020 年 9 月前猪肉价格数据的长期趋势因素、商业周期变化因素和季节性因素。采用 GM(1,1)模型对长期趋势因素和商业周期变化因素进行拟合和预测。分别将 GM(1,1)的拟合值和预测值乘以季节性因素和经验季节性因素，得到原始月度猪肉价格序列的拟合值和预测值。
GM(1,1)模型对长期趋势因素和商业周期变化因素的拟合和预测表达式为 X(1)(k)=-1704.80e-0.022(k-1)+1742.36。预测值的经验季节性因素为 1.002。使用 Census X12-GM(1,1)方法，2020 年 7 月至 12 月的猪肉价格最终预测值分别为 34.75、33.98、33.23、32.50、31.78 和 31.08。与 ARIMA、GM(1,1)和 Holt-Winters 模型相比，Census X12-GM(1,1)方法在预测部分的均方根误差（RMSE）、平均绝对百分比误差（MAPE）和平均绝对误差（MAE）最低。
与其他单一模型相比，Census X12-GM(1,1)方法对月度猪肉价格序列具有更好的预测精度。Census X12-GM(1,1)方法预测的月度猪肉价格可以作为利益相关者的重要参考。
