我国农业机械化作业水平的组合预测模型对比研究被引量：1

Comparative Study of Combination Forecasting Models on the Operation Level of Agricultural Mechanization

下载PDF

导出

摘要为探究不同组合模型对我国农业机械化作业水平预测的影响,以我国农业机械化作业水平时间序列为研究对象,以2001-2012年历史数据作为训练样本,分别选择指数曲线法、三次指数平滑法及灰色预测法构建单项预测模型,并基于单项模型的预测结果,选择误差平方和最小法、Shapley法和IOWGA法构建组合预测模型,对2013-2015年农业机械化作业水平进行预测。预测结果的对比分析表明:组合模型的预测精度从高到低分别为IOWGA组合模型、基于误差平方和最小法组合模型及Shapley组合模型。IOWGA组合预测模型充分汇集了各单项预测模型中的有效信息,且根据预测精度的大小赋予不同的权值,具备更好的预测效果和稳定性,相对误差可控制在1%,可用于我国农业机械化作业水平预测。 To explore the effects of different combination forecasting models on the operation level of agricultural mechanization, the time series of the operation level of agricultural mechanization and the historical data from 2001–2012 are selected as the research object and training sample, respectively, in this study. The exponential curve method, the cubic exponential smoothing method, and the gray forecasting method are adopted to construct the single forecasting model. Moreover, the least sum of squared error (LSSE), Shapley, and IOWGA operators are selected to construct the combination forecasting model and forecast the operation level of agricultural mechanization in 2013–2015 based on the forecasting results of the single model. The comparison results show that the forecasting effect of the IOWGA combination model is the best, followed by that of the combination forecasting model based on LSSE, whereas the Shapley combination model exhibits the lowest forecasting accuracy. The IOWGA combination forecasting model can collect valid information from each forecasting model and assign different weights according to the level of forecasting precision, while exhibiting better forecasting effect and stability. Its relative error can be controlled at 1%. The IOWGA combination forecasting model exhibits the advantage of forecasting the operation level of agricultural mechanization in China. Research results can provide effective references for agricultural mechanization development plan.

作者李鹏飞吕恩利陆华忠陈明林荀露 Li Pengfei;Lv Enli;Lu Huazhong;Chen Minglin;Xun Lu(College of Engineering, Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education,South China Agricultural University, Guangzhou 510642, China)

机构地区华南农业大学工程学院/南方农业机械与装备关键技术教育部重点实验室

出处《农机化研究》北大核心 2019年第3期1-7,共7页 Journal of Agricultural Mechanization Research

基金中国工程院咨询研究项目(2015)

关键词农业机械化作业水平组合模型预测 agricultural mechanization operation level combination model forecasting

分类号 S232.3 [农业科学—农业机械化工程]