A Development of Price Forecasting System by Neural Network
| Project/Area Number | 06660283 |
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
Agro-economics
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| Research Institution | Tottori University |
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Principal Investigator |
FUJII Yoshinori Tottori University Fac.Agric.Professor, 農学部, 教授 (20032097)
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| Co-Investigator(Kenkyū-buntansha) |
SEMBOKUYA Yasushi Tottori University Fac.Agic.Assistant Professor, 農学部, 講師 (50243382)
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| Project Period (FY) |
1994 – 1995
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| Project Status |
Completed(Fiscal Year 1995)
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| Budget Amount *help |
¥1,700,000 (Direct Cost : ¥1,700,000)
Fiscal Year 1995 : ¥300,000 (Direct Cost : ¥300,000)
Fiscal Year 1994 : ¥1,400,000 (Direct Cost : ¥1,400,000)
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| Keywords | Price forcasting / Neural Network / Demand for information / Market for agricultural products / 青果物価格予測 / 価格予測システム |
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| Research Abstract |
The aim of this research is to apply the neural network information processing theory to an analysis of demand structure of fruits and vegetables and examine the ability of neural network in non-linear analysis and its internal representations. Neural network has the ability to reproduce the input-output relationship, which generally hard to be understood, by appropriate learning process. After learning, the neural network used to be applied to simulation because it has obtained enough knowledge about sample data. But if the neural network can not make the internal representations of sample data precisely, there will be no grounds for simulation.
We used MR neural network which is a kind of modified feed-forward neural network and acts like Multiple Regression analysis. For the analysis, we input the quantity forwarded from six producing areas including Tottori prefecture and forwarding time as input data, and the actual prices of the vegetables produced in Tottori as teaching data, so that it could learn the relationship of these data. After the learning ended, we simulated the relation between quantity of forwarded and price.
The obtained results represented non-linearity of the demand structure. the internal representations of the neural network showed the demand structure of fruits and vegatables precisely. Thus, the non-linearity of the demand structure was given logical grounds.
The results shows that the price of vegetables has no linear relationship with quantity.
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Report
(3results)
Research Products
(5results)
