灰預測應用於台灣蔬果產地價格之分析─以愛文芒果為例

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doi:10.6844/NCKU.2011.00128

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灰預測應用於台灣蔬果產地價格之分析─以愛文芒果為例

An Application of Grey Analysis on the Taiwan Fruits and Vegetables Farm Price with Irwin Mango As an Example

容萍 , Masters　　Advisor：張淑昭　　

繁體中文 DOI： 10.6844/NCKU.2011.00128

企業資源規劃；價格預測；灰色理論；灰關聯分析； enterprise resource planning ； price forecasting ； grey theory ； the grey relational analysis

Abstract │ Reference (53) │ Times Cited (1) │ Altmetrics

Abstract 〈TOP〉

Parallel Abstract 〈TOP〉

Since 2002, Taiwan joined the WTO organization, government regulation in response to the WTO Agreement, gradually opening up of agricultural imports in some countries, neighboring countries of Southeast Asia, low agricultural prices, import liberalization, the severe impact on the domestic market, Taiwan is facing a saturated market of fruits and vegetables, competition and resource constraints of multiple challenges. Recently, because of the two sides signed a framework economic cooperation agreement (ECFA), the face of fruits and vegetables import quota control, how to strengthen and deepen the early warning mechanism, the market price of supply and demand balance, predict precisely is an important work for the government. 
     This study is based grey prediction theory, citing Agriculture and Food Agency Council of Agriculture Executive Yuan  89 years to 99 years of agricultural statistics bulletin information, use"Grey relational analysis," to select a high correlation with the prices of fruits and vegetables factors, such as the planting area, area, production costs and import and export and other influencing factors, then the origin price of the grey forecast, the last use of relevant variables of the original data to construct the price of fruit and vegetable origin prediction model, the results provide the public sector as the price of agricultural policy and industry guidance early warning of reference. Attainable results are as follows: 
(A)raw data will be affected by a number of factors which are changeable, the traditional GM (1,1) model can not predict the ups and downs with the data of the significant changes, so GM(1,N) model and the extend type of GM (1,N) model is superior in producing price forecasts GM (1,1) mode. 
(B)by cross-analysis showed that the correct type of GM(1,N)model has a smaller average error, and no matter in what combination, the extend type of GM (1, N) model in the large data variable ranges of predictive value is more accuracy than those of GM (1,1) and GM(1,N)model.

Reference ( 53 ) 〈TOP〉

2.Chih-Yung Tsai, Yih-Chearing,（2004）, The Study of Artificial Neural Network for the Prediction of the Yield of Perennial Crop.
連結：
3.Hsu, C. I. & Wen, Y. U.（1998） Improved grey prediction models for transpacific air passenger market. Transportation Planning and Technology, 22, 2, 87-107.
連結：
4.Hsu,C.C.& Chen,C.Y.（2003）, Applicatuons of improved grey prediction model for power demand forecasting. Energy Conversion and Management.
連結：
5.Jurgen D. Garbrecht, Jeanne M. Schneider.（2006）, Climate forecast and prediction Product dissemination for agriculture in the United States.CSIRO Published- Crop & Pasture Science
連結：
6.Kuo, R. J. & Xue, K. C.（1999）. Fuzzy neural networks with application to sales forecasting. Fuzzy Sets and Systems, 108, 123-143.
連結：

Times Cited (1) 〈TOP〉

林宜貞(2014)。應用灰預測建立茶園面積預測系統之研究。屏東科技大學農園生產系所學位論文。2014。1-0。

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