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    政大機構典藏 > 商學院 > 資訊管理學系 > 期刊論文 >  Item 140.119/71376


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    題名: Combining Artificial Intelligence with Non-linear Data Processing Techniques for Forecasting Exchange Rate Time Series
    作者: 楊亨利
    Yang, Heng-Li;Lin, Han-Chou
    貢獻者: 資管系
    關鍵詞: Back-propagation neural network (BPNN);Hilbert–Huang transform (HHT);Empirical mode decomposition (EMD);Intrinsic mode function (IMF)
    日期: 2012.04
    上傳時間: 2014-11-13 15:08:23 (UTC+8)
    摘要: Combing back-propagation neural network (BPNN) and empirical mode decomposition (EMD) techniques, this study proposes EMD-BPNN model for forecasting. In the first stage, the original exchange rate series were first decomposed into a finite, and often small, number of intrinsic mode functions (IMFs). In the second stage, kernel predictors such as BPNN were constructed for forecasting. Compared with traditional model (random walk), the proposed model performs best. This study significantly reduced errors not only in the derivation performance, but also in the direction performance.
    關聯: International Journal of Digital Content and its Application, 6(6), 276-283
    資料類型: article
    顯示於類別:[資訊管理學系] 期刊論文

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