Integrating application of independent component analysis and support vector machine in foreign exchange rate forecasting

被引：0

作者：

Han, Feng ^{[1
]}

Xie, Chi ^{[1
,2
]}

Ding, Lan ^{[3
]}

Wang, Gang-Jin ^{[1
]}

机构：

[1] Business School, Hunan University

[2] Center of Finance and Investment Management, Hunan University

[3] School of Economy and Trade, Hunan University

来源：

Advances in Information Sciences and Service Sciences | 2012年 / 4卷 / 04期

关键词：

Forecasting; Foreign exchange rate; Independent component; Support vector machine;

D O I：

10.4156/AISS.vol4.issue4.4

中图分类号：

学科分类号：

摘要：

Financial series forecasting is regarded as a challenging task due to its non-linearity, noisy and less prior information. We introduce a signal processing approach, independent component analysis (ICA), to extract the useful independent components as the inputs of support vector machine (SVM) to improve the prediction accuracy. And we compare the ICA method with the wavelet decomposition method in terms of predicting error and precision as well. The empirical results indicate that the proposed IC-SVM model outperforms single SVM, Wavelet-SVM and ARMA-GARCH model in foreign exchange rate forecasting.

引用

页码：27 / 34

页数：7

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