Research for construction and application of PCA-SVM for exchange rate forecasting
Pages 94 - 98
Abstract
The traditional SVM method has the problem of kernel function's parameters and dynamic optimization of penalty coefficient C. This paper constructs a hybrid model by extending the SVM method with PCA method to solve the problem. Finally we use the daily date of the exchange rate to test the high prediction accuracy of PCA-SVM model. In order to achieve better prediction accuracy, four kernel functions are used to construct different SVM. The empirical results show that SVR based on RBF kernel has the highest prediction accuracy. This result illustrates that the relevant government can take use of the model to monitor the smooth fluctuations in the exchange rate market.
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- Research for construction and application of PCA-SVM for exchange rate forecasting
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 25 August 2018
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- Research-article
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- Chengdu Polytechnic
- Sichuan Provincial Education Department
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IMMS 2018
IMMS 2018: 2018 International Conference on Information Management and Management Sciences
August 25 - 27, 2018
Chengdu, China
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