Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/78884


Title: Applying EMD-based neural network to forecast NTD/USD exchange rate
Authors: Yang, Heng-li;Lin, Han Chou
楊亨利
Contributors: 資訊管理學系
Keywords: Back propagation neural networks;Empirical mode decomposition;Exchange rate forecasting;Exchange rates;Hilbert Huang transforms;Intrinsic mode functions;Mean absolute percentage error;Random Walk;Backpropagation;Forecasting;Information management;Signal processing;Torsional stress;Neural networks
Date: 2011-06
Issue Date: 2015-10-08 17:16:46 (UTC+8)
Abstract: This study applied back-propagation neural network (BPNN) and empirical mode decomposition (EMD) techniques for forecasting exchange rate. The aim of this study is to examine the feasibility of the proposed EMD-BPNN model in exchange rate 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 are constructed for forecasting. It was demonstrated that the proposed model performs better than traditional model (random walk). The mean absolute percentage errors are significantly reduced. © 2011 AICIT.
Relation: Proceedings - 7th International Conference on Networked Computing and Advanced Information Management, NCM 2011
Data Type: conference
Appears in Collections:[資訊管理學系] 會議論文

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