基于指数平滑模型的农产品价格预测研究
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摘要
近年来,随着科学技术的飞速发展,经济和社会都取得了极大的进步,与此同时,在各个领域产生了大量的数据,其中也包括农产品价格数据。激增的数据背后隐藏着许多重要的信息。人们不再满足于数据库的查询功能,希望能够对其进行更高层次的分析,以便能从数据中提取信息或者知识为决策服务。
农产品价格受市场影响的程度特别大,特别是受农产品的供求关系影响较大,同时价格本身又受自然条件、社会和经济条件的影响,特别是国际市场的影响。从价格本身来看,受供求、季节等发生波动,受外界各种影响比较多,这就造成了价格预测的困难。但从长期看,农产品价格随着时间的推移仍然呈现一定规律性。
指数平滑一种数学上比较成熟的随机时间序列预测方法,已经在卫生、经济等领域得到广泛应用,它是目前公认的用于一个国家或地区经济预测中比较先进的适用的科学的时间序列分析模型之一。那么这种方法是否也适用于农产品的价格走势的预测呢?目前,这方面的研究的较少,本文就此课题进行了研究。
本文研究时间序列数据挖掘,通过对过去历史行为的客观记录分析,揭示其内在的规律,进而完成预测未来行为等决策性工作。通过研究时间序列分析法中的指数平滑法,预测农产品的价格趋势。
论文基于指数平滑法,通过历史上一段时间某地西红柿价格变化情况,建立西红柿的价格指数平滑模型,用该模型拟合西红柿的历史走势,根据模型预测该农产品未来几年的价格变化情况。试验结果表明,本方法能够有效地对西红柿价格曲线进行拟合,预测。
最后,为了实现农产品价格预测的自动化处理,设计并实现了一个基于Web数据挖掘的农产品价格搜集,查询,检索,预测系统。
In recent years, with the rapid development of science and technology, economy and society have made great progress, meanwhile a large mount of date such as agricultural prices have been produced in various fields. It has come to the point that many important information are behind the dramatically increasing of date. Now simply query with database is no longer satisfying our demand. Additionally we hope to be able to carry out data analysis over a higher level in order to extract useful information or knowledge from the large mount of data or knowledge for decision-making.
Prices of agricultural products are affected by the market in a high extent, especially the relationship between supply and demand of agricultural products, while the price itself especially in the international markets is subject to natural conditions, social and economic conditions. Therefore forecasting agriculture products prices shall be a difficult but significant job. Though price is influenced by the fluctuations of supply and demand, seasonal, region etc, it still has certain regularities when related to a long period.
Exponential smooth is a forecasting method using random time series which is quite mature in mathematical field. It has been widely used in health, economic and other fields, it is now recognized as one of the advanced madels which are applicative in economic forecasting for a country or a region. Whether this approach is also adaptive for forecasting agricultural products prices or not is ambiguous, so this theis is object to this subject.
We research time series data mining through the objectively analyze records of past acts in history, reveal its inherent la regular pattern, further more forecast future behavior which is available for decision-making. Our thesis is in view of time series analysis based on exponential smoothing method in order to forecast price trends of agricultural products.
This thesis is based on the exponential smooth model, we establish a tomatoes exponential smoothing model which according to its price fluctuation during a long time, fitting the model with the historical trend of tomatoes. And then we can obtain the price of tomatoes in following years according to this model. The results show out that this model can effectively carry out fitting and forecasting the price curve of tomatoes.
Finally, in order to achieve automatic processing in forecast prices of agricultural products, a Web-based data mining to collect the prices of agricultural products is designed and implemented, then this system can be realized query, retrieval, prediction.
农产品价格受市场影响的程度特别大,特别是受农产品的供求关系影响较大,同时价格本身又受自然条件、社会和经济条件的影响,特别是国际市场的影响。从价格本身来看,受供求、季节等发生波动,受外界各种影响比较多,这就造成了价格预测的困难。但从长期看,农产品价格随着时间的推移仍然呈现一定规律性。
指数平滑一种数学上比较成熟的随机时间序列预测方法,已经在卫生、经济等领域得到广泛应用,它是目前公认的用于一个国家或地区经济预测中比较先进的适用的科学的时间序列分析模型之一。那么这种方法是否也适用于农产品的价格走势的预测呢?目前,这方面的研究的较少,本文就此课题进行了研究。
本文研究时间序列数据挖掘,通过对过去历史行为的客观记录分析,揭示其内在的规律,进而完成预测未来行为等决策性工作。通过研究时间序列分析法中的指数平滑法,预测农产品的价格趋势。
论文基于指数平滑法,通过历史上一段时间某地西红柿价格变化情况,建立西红柿的价格指数平滑模型,用该模型拟合西红柿的历史走势,根据模型预测该农产品未来几年的价格变化情况。试验结果表明,本方法能够有效地对西红柿价格曲线进行拟合,预测。
最后,为了实现农产品价格预测的自动化处理,设计并实现了一个基于Web数据挖掘的农产品价格搜集,查询,检索,预测系统。
In recent years, with the rapid development of science and technology, economy and society have made great progress, meanwhile a large mount of date such as agricultural prices have been produced in various fields. It has come to the point that many important information are behind the dramatically increasing of date. Now simply query with database is no longer satisfying our demand. Additionally we hope to be able to carry out data analysis over a higher level in order to extract useful information or knowledge from the large mount of data or knowledge for decision-making.
Prices of agricultural products are affected by the market in a high extent, especially the relationship between supply and demand of agricultural products, while the price itself especially in the international markets is subject to natural conditions, social and economic conditions. Therefore forecasting agriculture products prices shall be a difficult but significant job. Though price is influenced by the fluctuations of supply and demand, seasonal, region etc, it still has certain regularities when related to a long period.
Exponential smooth is a forecasting method using random time series which is quite mature in mathematical field. It has been widely used in health, economic and other fields, it is now recognized as one of the advanced madels which are applicative in economic forecasting for a country or a region. Whether this approach is also adaptive for forecasting agricultural products prices or not is ambiguous, so this theis is object to this subject.
We research time series data mining through the objectively analyze records of past acts in history, reveal its inherent la regular pattern, further more forecast future behavior which is available for decision-making. Our thesis is in view of time series analysis based on exponential smoothing method in order to forecast price trends of agricultural products.
This thesis is based on the exponential smooth model, we establish a tomatoes exponential smoothing model which according to its price fluctuation during a long time, fitting the model with the historical trend of tomatoes. And then we can obtain the price of tomatoes in following years according to this model. The results show out that this model can effectively carry out fitting and forecasting the price curve of tomatoes.
Finally, in order to achieve automatic processing in forecast prices of agricultural products, a Web-based data mining to collect the prices of agricultural products is designed and implemented, then this system can be realized query, retrieval, prediction.
引文
[1]Robert Cooley,Bamshad Mobasherm,and Jaideep Srivastavam Web Mining:Information and Pattern Doscovery on the World Wide Web
[2]Mobasher B,Jain N,Han E H and Srivastava J 1996,Web Mining:Pattern Discovery from World Wide Web Transactions,Technical Report 96-050,1996 9 3
[3]张惠军.农产品价格支持农民增收的实证分析[J].集团经济研究,2007(32)
[4]吴德会.基准动态指数平滑的预测模型[J].统计与决策,2007,(19)
[5]姚霞,彭汉艮,朱艳,曹卫星,张卫建.时鲜农产品价格预测的 ARIMA 时序模型构造与应用[J].农业系统科学与综合研究,2007,23(1)
[6]Pandit,Wu xianmin.时间序列及系统分析与应用[M].李昌棋,容国俊.北京:机械工业出版社,1990:121-141
[7]James,W.Taylor.Volatility Forecasting with Smooth Transition Exponential smoothing.International Journal of Forecasting 2004,2(20):273-286
[8]Holt.C Forecasting Seasonal and Trends by Exponentially Weighted Moving Averages.PA7 Carnegie Institute of Technology Available from the Engineering Library,University of Texas at Austin,Internation Joural of Forecasting,1957,52(1):5-13
[9]Brown,Goodell.Smoothing Forecasting and Prediction of Discret Time Series.Englewood Cliffs,NJ7 Prentice-Hall,1963:3-24
[10]Klaus PotzlBerger,Leopold Sogner.Stochastic Equilibrium:Learning by Exponential Smoothing.Journal of Economic with Dynamics &Control,2003,10(27):1743-1770
[11]Holt.C.Forceasting Seasonals and Trends by Exponentially Weighted Moving Averages.Internation Journal of Forecasting,2004,20(1):5-10
[12]Holt.C Author's Retrospective and Forecasting Seasonals and Trends by Exponentially Weighted Moving Averages.International Journal of Forecasting,2004.20(1):11-13
[13]Gardner.Jr.Exponential Smoothing:The State of the Art.Journal of Forecasting,1985,4(4):1-28
[14]Everette.S,Gardner.Jr.Exponential smoothing:The State of the Art-Part Ⅱ.International Journal of Forecasting,2006,15(1):637-666
[15]扬叔子,吴雅.时间序列分析的工程应用[M].武汉:华中理工大学出版社,1990:1-31
[16]Trevor Hastie,Robert Tibshirani,Jerome Friedman.The Elements of Statistical learning Data Mining,Inference and Predictions.New York:Springer-Verlag,2001:1-18
[17]王燕.应用时间序列分析[M].北京:中国人民大学出版社,2005:1-12
[18]Tong.H.Non-linear Time Series.Oxford Statistical Science Series.Oxford University Press.New York(2 Edition),2002:20-35
[19]安鸿志,陈兆国,杜金观.时间序列的分析与应用[M].北京:科学出版社,1983:1-25
[20]黎锁平,刘坤会.指数平滑优化模型及其应用[J].系统工程学报,2003,18(2):163-167
[21]刁明碧.统计理论学[M]北京:中国科学技术出版社,1998.410-417
[22]雷淑玲,农产品价格上涨对农民收支的影响[J].经济论坛,(140):32-33
[23]靳明,赵昶.绿色农产品消费意愿的经济学分析[J].财经论丛,2007,(6)
[24]王丰效,销售量预测模型及其应用[J].天水师范学院学 2002,22(3):19-20
[25]杨茂盛,李银侠.季节性指数平滑法在库存商品需求预测中的应用[J].商场现代化,2007,(508)
[26]刘心报 关于指数平滑预测法精度的探讨[J].安徽工学院学报 1994,13(3):81-84
[27]马玉宝.基于数据仓库的知识发现模式的研究[D].合肥:合肥工业大学,2003
[28]丁声俊.从深层次认识和看待农产品价格变动[J].价格理论与实践
[29]陈红叶.Web信息提取及知识发现方法研究[D].合肥:合肥工业大学,2002
[30]米翠兰.二次指数平滑法的研究与应用[J].华北煤炭医学院学报,2000,2(5):524-525
[31]瞿继合.基于Web的数据挖掘技术研究[D].沈阳:东北财经大学,2004
[32]陈小红.回归分析在经济预测中的应用[J].铜陵财经专科学报2000,4
[33]韩小光等.非线性回归模型及其应用[J].内蒙古统计,1998,5
[34]周纪芗.回归分析.华东师范大学出版社[J],1993
[35]张崇甫等.统计分析方法及其应用[J].重庆大学出版社,1995
[36]陈日进.销售预测中指数平滑法与时间序列分解法的比较[J],2007.7(4):40-43
[37]Everett.S,Gardner.Jr.Rule-Based Forecasting Vs.Damped-Trend Exponential Smoothing.Management Science,1999,45(8):1169-1176
[38]王慈光.二次指数平滑法中确定初始值的简便方法[J].西南交通大学学报,2004,39(3)
[39]朱立学,罗锡文,臧英,区颖刚.基于3次指数平滑的水稻生产综合机械化发展水平预测[J].农机化研究,2007,7(7)
[40]袁亚丽,郭喜凤.利用Excel进行市场预测分析[J].商场现代化,2007.8(513)
[41]罗来军.指数平滑模型在成本预测中的应用研究[J].企业家天地理论版,2007.2
[42]王梦东,刘敏.指数平滑模型在能源消费总量预测中的应用[J].武汉船舶职业技术学院学报,2007,(3)
[43]范明,孟小峰等.数据挖掘概念与技术[M].北京:机械工业出版社,2001.8
[44]Fayyad U,Piatersky-Shapiro G and Smyth P,Knowledge Discovery and Data Mining:Towards a Unifying Framework,Proceedings of the Second Inernational conference on Konwledge Discovery and Data Mining(KDD-96),Portland,Oregon,August 2-4,1996,AAA Ⅰ Press
[45]张忠平.指数平滑法[M].北京:中国统计出版社,1996:1-25
[46]黄荣富,真宏.三次指数平滑法在港口吞吐量预测中的应用[J].水运管理,2003,(2):13
[47]徐大江.预测模型参数的指数平滑估计法及其应用的进一步研究[J].系统工程理论与实践,1999,19(2):25-30
[48]高仁祥,张世英,刘豹.非线性经济建模与预测新思路探讨[J].系统工程理论与实践,1997,(7):7-11
[49]马玉春等.Web 中文文本分词技术研究[J].计算机应用,2004,(4)
[50]张丽,牛惠芳.时间序列分析方法在居民消费价格指数预测中的应用[J].洛阳师范学院学报,2008,(2):29-31
[2]Mobasher B,Jain N,Han E H and Srivastava J 1996,Web Mining:Pattern Discovery from World Wide Web Transactions,Technical Report 96-050,1996 9 3
[3]张惠军.农产品价格支持农民增收的实证分析[J].集团经济研究,2007(32)
[4]吴德会.基准动态指数平滑的预测模型[J].统计与决策,2007,(19)
[5]姚霞,彭汉艮,朱艳,曹卫星,张卫建.时鲜农产品价格预测的 ARIMA 时序模型构造与应用[J].农业系统科学与综合研究,2007,23(1)
[6]Pandit,Wu xianmin.时间序列及系统分析与应用[M].李昌棋,容国俊.北京:机械工业出版社,1990:121-141
[7]James,W.Taylor.Volatility Forecasting with Smooth Transition Exponential smoothing.International Journal of Forecasting 2004,2(20):273-286
[8]Holt.C Forecasting Seasonal and Trends by Exponentially Weighted Moving Averages.PA7 Carnegie Institute of Technology Available from the Engineering Library,University of Texas at Austin,Internation Joural of Forecasting,1957,52(1):5-13
[9]Brown,Goodell.Smoothing Forecasting and Prediction of Discret Time Series.Englewood Cliffs,NJ7 Prentice-Hall,1963:3-24
[10]Klaus PotzlBerger,Leopold Sogner.Stochastic Equilibrium:Learning by Exponential Smoothing.Journal of Economic with Dynamics &Control,2003,10(27):1743-1770
[11]Holt.C.Forceasting Seasonals and Trends by Exponentially Weighted Moving Averages.Internation Journal of Forecasting,2004,20(1):5-10
[12]Holt.C Author's Retrospective and Forecasting Seasonals and Trends by Exponentially Weighted Moving Averages.International Journal of Forecasting,2004.20(1):11-13
[13]Gardner.Jr.Exponential Smoothing:The State of the Art.Journal of Forecasting,1985,4(4):1-28
[14]Everette.S,Gardner.Jr.Exponential smoothing:The State of the Art-Part Ⅱ.International Journal of Forecasting,2006,15(1):637-666
[15]扬叔子,吴雅.时间序列分析的工程应用[M].武汉:华中理工大学出版社,1990:1-31
[16]Trevor Hastie,Robert Tibshirani,Jerome Friedman.The Elements of Statistical learning Data Mining,Inference and Predictions.New York:Springer-Verlag,2001:1-18
[17]王燕.应用时间序列分析[M].北京:中国人民大学出版社,2005:1-12
[18]Tong.H.Non-linear Time Series.Oxford Statistical Science Series.Oxford University Press.New York(2 Edition),2002:20-35
[19]安鸿志,陈兆国,杜金观.时间序列的分析与应用[M].北京:科学出版社,1983:1-25
[20]黎锁平,刘坤会.指数平滑优化模型及其应用[J].系统工程学报,2003,18(2):163-167
[21]刁明碧.统计理论学[M]北京:中国科学技术出版社,1998.410-417
[22]雷淑玲,农产品价格上涨对农民收支的影响[J].经济论坛,(140):32-33
[23]靳明,赵昶.绿色农产品消费意愿的经济学分析[J].财经论丛,2007,(6)
[24]王丰效,销售量预测模型及其应用[J].天水师范学院学 2002,22(3):19-20
[25]杨茂盛,李银侠.季节性指数平滑法在库存商品需求预测中的应用[J].商场现代化,2007,(508)
[26]刘心报 关于指数平滑预测法精度的探讨[J].安徽工学院学报 1994,13(3):81-84
[27]马玉宝.基于数据仓库的知识发现模式的研究[D].合肥:合肥工业大学,2003
[28]丁声俊.从深层次认识和看待农产品价格变动[J].价格理论与实践
[29]陈红叶.Web信息提取及知识发现方法研究[D].合肥:合肥工业大学,2002
[30]米翠兰.二次指数平滑法的研究与应用[J].华北煤炭医学院学报,2000,2(5):524-525
[31]瞿继合.基于Web的数据挖掘技术研究[D].沈阳:东北财经大学,2004
[32]陈小红.回归分析在经济预测中的应用[J].铜陵财经专科学报2000,4
[33]韩小光等.非线性回归模型及其应用[J].内蒙古统计,1998,5
[34]周纪芗.回归分析.华东师范大学出版社[J],1993
[35]张崇甫等.统计分析方法及其应用[J].重庆大学出版社,1995
[36]陈日进.销售预测中指数平滑法与时间序列分解法的比较[J],2007.7(4):40-43
[37]Everett.S,Gardner.Jr.Rule-Based Forecasting Vs.Damped-Trend Exponential Smoothing.Management Science,1999,45(8):1169-1176
[38]王慈光.二次指数平滑法中确定初始值的简便方法[J].西南交通大学学报,2004,39(3)
[39]朱立学,罗锡文,臧英,区颖刚.基于3次指数平滑的水稻生产综合机械化发展水平预测[J].农机化研究,2007,7(7)
[40]袁亚丽,郭喜凤.利用Excel进行市场预测分析[J].商场现代化,2007.8(513)
[41]罗来军.指数平滑模型在成本预测中的应用研究[J].企业家天地理论版,2007.2
[42]王梦东,刘敏.指数平滑模型在能源消费总量预测中的应用[J].武汉船舶职业技术学院学报,2007,(3)
[43]范明,孟小峰等.数据挖掘概念与技术[M].北京:机械工业出版社,2001.8
[44]Fayyad U,Piatersky-Shapiro G and Smyth P,Knowledge Discovery and Data Mining:Towards a Unifying Framework,Proceedings of the Second Inernational conference on Konwledge Discovery and Data Mining(KDD-96),Portland,Oregon,August 2-4,1996,AAA Ⅰ Press
[45]张忠平.指数平滑法[M].北京:中国统计出版社,1996:1-25
[46]黄荣富,真宏.三次指数平滑法在港口吞吐量预测中的应用[J].水运管理,2003,(2):13
[47]徐大江.预测模型参数的指数平滑估计法及其应用的进一步研究[J].系统工程理论与实践,1999,19(2):25-30
[48]高仁祥,张世英,刘豹.非线性经济建模与预测新思路探讨[J].系统工程理论与实践,1997,(7):7-11
[49]马玉春等.Web 中文文本分词技术研究[J].计算机应用,2004,(4)
[50]张丽,牛惠芳.时间序列分析方法在居民消费价格指数预测中的应用[J].洛阳师范学院学报,2008,(2):29-31