灰色预测在农业生产价格中的应用

Jiajun Zong, Quanyin Zhu
{"title":"灰色预测在农业生产价格中的应用","authors":"Jiajun Zong, Quanyin Zhu","doi":"10.1109/MINES.2012.78","DOIUrl":null,"url":null,"abstract":"In order to get the excellent accuracy for price forecast in the agriculture products market, the Grey Prediction method is utilized to forecast the price of the agriculture products in this paper. Ten agriculture products, which extracted from Agricultural Bank of China at January, 2011 to December 2011, are selected to forecast the price about four weeks and compare the Mean Absolute Percentage Errors (MAPE) by Grey Method (GM) and RBF Neural Network (NN). Experiments demonstrate that the GM(1, 1) is not good for forecasting the agriculture products price and is not stable too. While the RBF NN is better then the GM(1, 1). Experiment results prove that this verdict is meaningful and useful to analyze and to research the price forecast in the agriculture products market.","PeriodicalId":208089,"journal":{"name":"2012 Fourth International Conference on Multimedia Information Networking and Security","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Apply Grey Prediction in the Agriculture Production Price\",\"authors\":\"Jiajun Zong, Quanyin Zhu\",\"doi\":\"10.1109/MINES.2012.78\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to get the excellent accuracy for price forecast in the agriculture products market, the Grey Prediction method is utilized to forecast the price of the agriculture products in this paper. Ten agriculture products, which extracted from Agricultural Bank of China at January, 2011 to December 2011, are selected to forecast the price about four weeks and compare the Mean Absolute Percentage Errors (MAPE) by Grey Method (GM) and RBF Neural Network (NN). Experiments demonstrate that the GM(1, 1) is not good for forecasting the agriculture products price and is not stable too. While the RBF NN is better then the GM(1, 1). Experiment results prove that this verdict is meaningful and useful to analyze and to research the price forecast in the agriculture products market.\",\"PeriodicalId\":208089,\"journal\":{\"name\":\"2012 Fourth International Conference on Multimedia Information Networking and Security\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-11-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 Fourth International Conference on Multimedia Information Networking and Security\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MINES.2012.78\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Fourth International Conference on Multimedia Information Networking and Security","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MINES.2012.78","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13

摘要

为了使农产品市场的价格预测具有较高的准确性,本文采用灰色预测法对农产品价格进行预测。选取中国农业银行2011年1月至12月10个农产品品种,采用灰色方法(GM)和RBF神经网络(NN)对其进行4周左右的价格预测,并比较其平均绝对百分比误差(MAPE)。实验表明,GM(1,1)对农产品价格的预测效果不佳,且不稳定。而RBF神经网络优于GM(1,1)。实验结果证明,该结论对分析和研究农产品市场的价格预测具有重要意义和实用价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Apply Grey Prediction in the Agriculture Production Price
In order to get the excellent accuracy for price forecast in the agriculture products market, the Grey Prediction method is utilized to forecast the price of the agriculture products in this paper. Ten agriculture products, which extracted from Agricultural Bank of China at January, 2011 to December 2011, are selected to forecast the price about four weeks and compare the Mean Absolute Percentage Errors (MAPE) by Grey Method (GM) and RBF Neural Network (NN). Experiments demonstrate that the GM(1, 1) is not good for forecasting the agriculture products price and is not stable too. While the RBF NN is better then the GM(1, 1). Experiment results prove that this verdict is meaningful and useful to analyze and to research the price forecast in the agriculture products market.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信