Building a Model for Prediction Exchange Rate from USD to VND Using a Novel Method
American Journal of Electrical and Computer Engineering
Volume 4, Issue 2, December 2020, Pages: 55-61
Received: Sep. 10, 2020;
Accepted: Sep. 23, 2020;
Published: Oct. 13, 2020
Views 163 Downloads 70
Nghien Nguyen Ba, Information Technology Faculty, Ha Noi University of Industry, Ha Noi, Vietnam
Cuong Nguyen Thai, Information Technology Faculty, Ha Noi University of Industry, Ha Noi, Vietnam
Huyen Le Xuan, Foreign Language Faculty, Ha Noi University of Industry, Ha Noi, Vietnam
Nhung Nguyen Thi, Information Technology Faculty, Ha Noi University of Industry, Ha Noi, Vietnam
Phuong Pham Thi Kim, Information Technology Faculty, Ha Noi University of Industry, Ha Noi, Vietnam
Thuy Ngo Thi Bich, Information Technology Faculty, Ha Noi University of Industry, Ha Noi, Vietnam
In this paper, the combination of the Hilbert-Huang Transform (HHT), Support Vector Regression (SVR) and an embedding theorem is described to predict the short-term exchange rate from United States dollar to Vietnamese Dong. Firstly, we use Empirical Mode Decomposition (EMD) of the HHT to decompose a signal into multi oscillation scales called Intrinsic Mode Function (IMF). After that, we synthesis the signal without highest oscillation IFM to reduce noise. Next, we use the False nearest neighbors algorithm to find the embedding dimension space of the de-noise signal. Finally, we use SVR to build a model for prediction exchange rate between US dollar and VND. By using the Hilbert-Huang Transform as an adaptive filter, the proposed method decreases the embedding dimension space from twelve (original samples) to four (de-noising samples). This dimension space provides the number of inputs to the SVR model, which affects the complexity and the training time decrease of the model. Experimental results indicated that this method not only reduces complication of the model but also achieves higher accuracy prediction than the direct use of original data.
Nghien Nguyen Ba,
Cuong Nguyen Thai,
Huyen Le Xuan,
Nhung Nguyen Thi,
Phuong Pham Thi Kim,
Thuy Ngo Thi Bich,
Building a Model for Prediction Exchange Rate from USD to VND Using a Novel Method, American Journal of Electrical and Computer Engineering.
Vol. 4, No. 2,
2020, pp. 55-61.
Hua, X., Zhang, D., Leung, S. C. H.: Exchange rate prediction through ANN based on Kernel Regression. 2010 Third International Conference on Business Intelligence and Financial Engineering. August 13-15, 2010, 39-43.
Hanias, M. P., Curtis, P. G.: Time Series Prediction of Dollar\Euro Exchange Rate Index. International Research Journal of Finance and Economics. May 2008, Issue 15, 224-231.
Liu, F.-Y.: The Hybrid Prediction Model of CNY/USD Exchange Rate Based on Wavelet and Support Vector Regression. 2010 2nd International Conference on Advanced Computer Control (ICACC). March 27-29, 2010, 561-565.
Iokibe, T., Murata, S., Koyama, M.: Prediction of Foreign Exchange Rate by Local Fuzzy Reconstruction Method. Systems, Man and Cybernetics, 1995. IEEE International Conference on Intelligent Systems for the 21st Century, vol. 5, Oct. 22-25, 1995, 4051–4054.
Božic, J., Vukotic, S., Babic, Đ.: Prediction of the RSD exchange rate by using wavelets and neural networks. 2011 19th Telecommunications forum (TELFOR), Nov. 22-24, 2011, 703–706.
Liu, W.: Forecasting exchange rate change between usd and jpy by using dynamic adaptive neuron-fuzzy logic system. Asia Pacific Journal of Finance and Banking Research. 2 (2) (2008) 1-12.
Zhang, L., Wu, D., Zhi, L.: Method of removing noise from EEG signals based on HHT method. The 1st International Conference on Information Science and Engineering (ICISE 2009). Dec. 26-28, 2009, 596–599.
Huang, N. E., Wu, M.-L., Qu, W., Long, S. R., Shen, S. S. P.: Applications of Hilbert–Huang transform to non-stationary financial time series analysis. Applied stochastic models in business and industry. 19 (3) 245–268.
Huang, N. E., Shen, S. S. P.: Hilbert-Huang Transform and Its Application. World Scientific Pub Co Inc, 2005.
Support Vector Regression–Data Mining Map https://www.saedsayad.com/support_vector_machine_reg.htm.
Ababarnel, H. D. I., Brown, R., Sidorowich, J. J., Tsimring, L. S.: The analysis of observed chaotic data in physical systems. Reviews of Modern Physics. 65 (4) (1993) 1331-1392.
Rodríguez, R., Bila, J., Mexicano, A., Cervantes, S., Ponce, R., Nghien, N. B.: Hilbert-Huang transform and neural networks for electrocardiogram modeling and prediction. In 2014 10th International Conference on Natural Computation (ICNC). IEEE. (2014) 561-567.
Nayab D., Muhammad Khan G., Mahmud S. A. (2013) Prediction of Foreign Currency Exchange Rates Using CGPANN. In: Iliadis L., Papadopoulos H., Jayne C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41013-0_10.
Kumar Chandar S, Sumathi Mahadevan, S. N. Sivanandam, Yan (2015) Forecasting of foreign currency exchange rate using neural network. International Journal on Computer Science and Engineering 7 (1): 99-108.
Nghien, N. B, Rodríguez, R.: Building an Early Warning Model for Detecting Environmental Pollution of Wastewater in Industrial Zones. Proceedings of the 14th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC-2019) 828-837.