Comparative Study: Common ANN and LS-SVM Exchange Rate Performance Prediction

SUN Ai; ZHAO Tianyi; CHEN Jungfang; CHANG Juifang

doi:10.1049/cje.2018.01.003

Volume 27 Issue 3

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SUN Ai, ZHAO Tianyi, CHEN Jungfang, et al., “Comparative Study: Common ANN and LS-SVM Exchange Rate Performance Prediction,” Chinese Journal of Electronics, vol. 27, no. 3, pp. 561-564, 2018, doi: 10.1049/cje.2018.01.003

Citation:

SUN Ai, ZHAO Tianyi, CHEN Jungfang, et al., “Comparative Study: Common ANN and LS-SVM Exchange Rate Performance Prediction,” Chinese Journal of Electronics, vol. 27, no. 3, pp. 561-564, 2018, doi: 10.1049/cje.2018.01.003

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Comparative Study: Common ANN and LS-SVM Exchange Rate Performance Prediction

doi: 10.1049/cje.2018.01.003

1.
Department of International Business, KUAS, Kaohsiung City 807, China;
2.
Department of Computer Science, Harbin Institute of Technology, Harbin 150001, China

More Information

Corresponding author: ZHAO Tianyi (corresponding author) received the B.S. degree and M.S. degrees from Harbin Institute of Technology (HIT). Now, he is a Ph.D. candidate of HIT. His research interests include data-driven prognostics and health management and bioinformatics. (Email:zty2009@hit.edu.cn)
Received Date: 2017-03-27
Rev Recd Date: 2017-07-03
Publish Date: 2018-05-10

Abstract

Abstract

Due to the mathematical modeling principle deficiency, the data-driven neural network and support vector machine methods have become the powerful basic methods for the exchange rate prediction. Based on the analysis of the characteristics of exchange rate time series data, the exchange rate prediction performance of Artificial neural network (ANN) and Least squares-Support vector machine (LS-SVM) is explored. The parameter optimization method of the two-times training is proposed. The fundamental principle of LS-SVM prediction is analysed in detail. By virtue of daily, monthly and quarterly data of three currency exchange rates, the prediction performance of LS-SVM is examined. The comparison is made with ANN prediction results based on the same data in relevant literature review. According to the experimental result, LS-SVM has better short-term prediction performance, and it is superior to ANN in most cases in terms of prediction precision.
- Least squares-Support vector machine (LS-SVM),
- Neural network,
- Structural risk function,
- Characteristic space mapping,
- Exchange rate prediction

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References(12)

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