| Project/Area Number |
05660251
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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 |
KANAYAMA Toshihisa Tottori Univ., Agriculture, Assistant professor, 農学部, 講師 (00214445)
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| Co-Investigator(Kenkyū-buntansha) |
SEMBOKUYA Yasushi Tottori Univ., Agriculture, Research associate, 農学部, 助手 (50243382)
KASAHARA Kozo Tottori Univ., Agriculture, Professor, 農学部, 教授 (60135837)
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| Project Period (FY) |
1993 – 1994
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| Project Status |
Completed (Fiscal Year 1994)
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| Budget Amount *help |
¥1,900,000 (Direct Cost: ¥1,900,000)
Fiscal Year 1994: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 1993: ¥1,100,000 (Direct Cost: ¥1,100,000)
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| Keywords | Fuzzy numbers / Fuzzy linear regression / Fuzzy reasoning / Menbership function / Tolerance level / Wholesale Market / Price forecasting system / Vegetables and fruits / ファジィ線形需給モデル / ファジィ青果物価格予測システム / 価格の予測誤差の許容範囲 / 天気情報 / 作付け・生育情報 / ±10% / ±8〜20% / ファジィ理論 / 価格予測モデル / 予約取引 / 前日セリ取引 / 予測精度 / 値決め能力 / 卸売市場 |
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| Research Abstract |
The necessities of price forecasting of wholesale markets, agricultural co-operatives and farms were investigated and a prototype of fuzzy price forecasting system of vegetables and fruits was developed in this research. The results were as follows.
Wholesale markets for vegetables and fruits have come to price their goods before they arrive at the markets, because the markets try to sell some vegetables only to those who have ordered a day in advance, and for some other reasons. Therefore wholesale, markets need price forecasting.
Agricultural co-operatives need price forecasting. The tolerance levels are <plus-minus>10 percents. The farms who produce Chinese yams in Tottori Prefecture need price forecasting, too. The tolerance levels are from <plus-minus>8 percents to <plus-minus>20 per
A prototype of fuzzy price forcasting system that was developed in this reseach has steps. In the first step, the fuzzy linear demand and supply models that have parameters of fuzzy numbers are estimated by fuzzy linear regression, and wholesale price after one year is forecasted means of these models. The form of membership function has an effect on price forecasting. In the second step, the forecasting price in the first step is corrected by means of fuzzy reasoning. The information of wather after one month and the planting area of the other production centers is needed in this step. If they are collected properly, the price that is forecasted in the first step can be corrected.
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