Chia Nan University of Pharmacy & Science Institutional Repository:Item 310902800/22785
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    CNU IR > Chna Nan Annual Bulletin > Vol.35 (2009) >  Item 310902800/22785
    Please use this identifier to cite or link to this item: https://ir.cnu.edu.tw/handle/310902800/22785


    Title: 以類神經網路建構白銀價格預測模式
    Constructing Silver Price Forecasting Model with Neural Networks
    Authors: 陳美惠
    許雅惠
    蔡坤穆
    Contributors: 化妝品應用與管理系
    Keywords: 倒傳遞類神經網路
    預測模型
    白銀價格
    投資性需求因素
    Back-Propagation Network
    Forecasting Model
    Silver Price
    Investment Factor
    Date: 2009
    Issue Date: 2010-09-10 09:09:05 (UTC+8)
    Abstract: 貴金屬材料中的白銀由於具備絕佳且多功能的材料特性,使其在製造產業上的需求大增。而此也導致白銀在投機性高的投資市場中,成為熱門的操作產物,使得白銀的價格大幅上升,造成產業需求者沈重的採購成本壓力。因此,如果能在考量投資需求的因素下,發展出能夠準確預測白銀價格的模型,將可提供白銀採購者決策時的參考。本研究應用倒傳遞類神經網路(Back-Propagation Neural Nt Networks, BPNN),來預測每日白銀的價格,以協助產業需求者,能夠將預測所得的價格實際應用於每日的採購工作中。模型的輸入變數包括直接性、間接性及投資需求因素,在經由不同網路架構及學習次數與學習率的調整與測試後,本研究提出一個較佳的BPNN模型來進行後續的相關實驗。結果顯示,本研究所建構的倒傳遞類神經網路模型,在連續30日滾動的驗證期間,其平均預測的準確率達到98.2%以上,證明該網路模型可有效幫助採購者來預測白銀的價格。
    Among the precious materials, silver has some unique and superior characteristics that cause the demand for it in industry to boost in recent years. Because silver is in a highly speculative market of investment, its price goes up andvariates substantially at the same time. As a result, the industry is now conhonting with high cost pressurtre for pwchasing silver; therefore, how to accurately predict the prices of silver becomes a critical challenge for companies in demand for silver. This study utilizes back-propagation anificial neural networks (BPNN) to predict daily silver price so that practitioners can apply the predicted prices to their purchases. We employ direct-related, indirect-related and investment factors as the inputs to the BPNN. After perfonning different combinations of network structure and leaming rate, a reasonably good BPTW`J model is proposed for the experiment. To test the accuracy of predictected silver prices, the model was executed for a period of consecutive 30 days and an average prediction accuracy of 98.2% was obtained. The results demonstrate the effectiveness of our BPNN model in predicting silver prices.
    Relation: 嘉南學報(科技類) 35:p.238-252
    Appears in Collections:[Chna Nan Annual Bulletin] Vol.35 (2009)
    [Dept. of Cosmetic Science and institute of cosmetic science] Periodical Articles

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