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中国农学通报 ›› 2014, Vol. 30 ›› Issue (14): 310-314.doi: 10.11924/j.issn.1000-6850.2013-2904

所属专题: 畜牧兽医

• 农业科技信息 • 上一篇    下一篇

灰色系统理论在生猪价格预测中的应用

郝妙 傅新红 陈蓉   

  • 收稿日期:2013-11-06 修回日期:2013-11-30 出版日期:2014-05-15 发布日期:2014-05-15
  • 基金资助:
    四川省农村发展研究中心基金项目 “四川省生猪价格波动的综合分析与预测” (CR1214)。

Application of Grey System Theory in Pig Price Prediction

  • Received:2013-11-06 Revised:2013-11-30 Online:2014-05-15 Published:2014-05-15

摘要: 近年来生猪价格波动明显,波动幅度越来越大,引起社会各界广泛关注。鉴于生猪价格的特殊性,运用灰色系统理论,根据生猪价格的历史数据,建立基于弱化缓冲算子的GM(1,1)预测模型,并通过残差、级比偏差、关联度和后验差检测对模型的合理性和精度进行误差检验,最后应用模型预测了未来3年的生猪价格。结果表明:P0=97.33%>90%、ρ(avg)=4.6613%<10%、r=0.8441>0.6和P=0.7778。由此可见,灰色系统理论GM(1,1)适用于生猪价格预测具有较高的精度。预测2014、2015和2016年生猪价格分别为16.72、15.26和17.42元/kg,与2013年相比较,分别上涨为14.84%、4.81%和19.64%。预测结果显示,未来生猪价格将在波动中上涨。

关键词: 透明度, 透明度

Abstract: In recent years, pig price becomes increasingly abnormal fluctuant, which causes widespread concerns in the whole society. Based on historical price data, pig price prediction applies scientific quantitative methods to predict future pig price. Therefore, price prediction can help support scientific decision-making for pig producers. Given the particularity of pig price, the article applied grey system theory, based on the weakening buffer operator and set up GM (1, 1) prediction model, then used residual error, grade ratio deviation, correlation degree and posteriori error four kinds of error checking methods to test the reasonability and the prediction accuracy. The error test results are P0=97.33%>90%、ρ(avg)=4.6613%<10%、r=0.8441>0.6 and P=0.7778. The error test results indicate that the gray system theory is appropriate when applies to pig price prediction, and has high prediction accuracy. According to GM (1, 1) prediction model the pig price of the next three years hog prices are 16.72 Yuan/kg, 15.26 Yuan/kg, and 17.42 Yuan/kg respectively. Comparing to 2013, these prices increase 14.84% , 4.81% and 19.64% respectively. Prediction results show that there exist some fluctuations in future pig price, and pig price will increase gradually.