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政大典藏 > College of Commerce > Department of MIS > Periodical Articles > Item 140.119/71376

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/71376

Title:	Combining Artificial Intelligence with Non-linear Data Processing Techniques for Forecasting Exchange Rate Time Series
Authors:	楊亨利 Yang, Heng-Li;Lin, Han-Chou
Contributors:	資管系
Keywords:	Back-propagation neural network (BPNN);Hilbert–Huang transform (HHT);Empirical mode decomposition (EMD);Intrinsic mode function (IMF)
Date:	2012.04
Issue Date:	2014-11-13 15:08:23 (UTC+8)
Abstract:	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.
Relation:	International Journal of Digital Content and its Application, 6(6), 276-283
Data Type:	article
Appears in Collections:	[Department of MIS] Periodical Articles

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