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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/17078

Title: Chaos-based Support Vector Regressions for Exchange Rate Forecasting
Authors: Huang, Shian-Chang;Chuang, Pei-Ju;Wu, Cheng-Feng;Lai, Hiuen-Jiun
Contributors: 企業管理學系
Keywords: Chaos theory;Hybrid model;Support vector machine;Exchange rate forecasting;Kernel method
Date: 2010-12
Issue Date: 2013-07-11T09:04:56Z
Publisher: Elsevier Ltd.
Abstract: This study implements a chaos-based model to predict the foreign exchange rates. In the first stage, the delay coordinate embedding is used to reconstruct the unobserved phase space (or state space) of the exchange rate dynamics. The phase space exhibits the inherent essential characteristic of the exchange rate and is suitable for financial modeling and forecasting. In the second stage, kernel predictors such
as support vector machines (SVMs) are constructed for forecasting. Compared with traditional neural networks, pure SVMs or chaos-based neural network models, the proposed model performs best. The rootmean- squared forecasting errors are significantly reduced.
Relation: Expert Systems with Applications, 37(12): 8590-8598
Appears in Collections:[企業管理學系] 期刊論文

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