Building a Model for Prediction Exchange Rate from USD to VND Using a Novel Method

Nghien Nguyen Ba; Cuong Nguyen Thai; Huyen Le Xuan; Nhung Nguyen Thi; Phuong Pham Thi Kim; Thuy Ngo Thi Bich

doi:doi:10.11648/j.ajece.20200402.14

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Building a Model for Prediction Exchange Rate from USD to VND Using a Novel Method

Nghien Nguyen Ba, Cuong Nguyen Thai, Huyen Le Xuan, Nhung Nguyen Thi, Phuong Pham Thi Kim, Thuy Ngo Thi Bich

Published in American Journal of Electrical and Computer Engineering (Volume 4, Issue 2)

Received: 10 September 2020 Accepted: 23 September 2020 Published: 13 October 2020

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Abstract

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.

Published in	American Journal of Electrical and Computer Engineering (Volume 4, Issue 2)
DOI	10.11648/j.ajece.20200402.14
Page(s)	55-61
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

SVR, Embedding Dimension Space, HHT, Average Mutual Information, Prediction

References

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[4]	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.
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[6]	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.
[7]	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.
[8]	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.
[9]	Huang, N. E., Shen, S. S. P.: Hilbert-Huang Transform and Its Application. World Scientific Pub Co Inc, 2005.
[10]	Support Vector Regression–Data Mining Map https://www.saedsayad.com/support_vector_machine_reg.htm.
[11]	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.
[12]	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.
[13]	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.
[14]	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.
[15]	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.

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APA Style

Nghien Nguyen Ba, Cuong Nguyen Thai, Huyen Le Xuan, Nhung Nguyen Thi, Phuong Pham Thi Kim, et al. (2020). Building a Model for Prediction Exchange Rate from USD to VND Using a Novel Method. American Journal of Electrical and Computer Engineering, 4(2), 55-61. https://doi.org/10.11648/j.ajece.20200402.14

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ACS Style

Nghien Nguyen Ba; Cuong Nguyen Thai; Huyen Le Xuan; Nhung Nguyen Thi; Phuong Pham Thi Kim, et al. Building a Model for Prediction Exchange Rate from USD to VND Using a Novel Method. Am. J. Electr. Comput. Eng. 2020, 4(2), 55-61. doi: 10.11648/j.ajece.20200402.14

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AMA Style

Nghien Nguyen Ba, Cuong Nguyen Thai, Huyen Le Xuan, Nhung Nguyen Thi, Phuong Pham Thi Kim, et al. Building a Model for Prediction Exchange Rate from USD to VND Using a Novel Method. Am J Electr Comput Eng. 2020;4(2):55-61. doi: 10.11648/j.ajece.20200402.14

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@article{10.11648/j.ajece.20200402.14,
  author = {Nghien Nguyen Ba and Cuong Nguyen Thai and Huyen Le Xuan and Nhung Nguyen Thi and Phuong Pham Thi Kim and Thuy Ngo Thi Bich},
  title = {Building a Model for Prediction Exchange Rate from USD to VND Using a Novel Method},
  journal = {American Journal of Electrical and Computer Engineering},
  volume = {4},
  number = {2},
  pages = {55-61},
  doi = {10.11648/j.ajece.20200402.14},
  url = {https://doi.org/10.11648/j.ajece.20200402.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajece.20200402.14},
  abstract = {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.},
 year = {2020}
}

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TY  - JOUR
T1  - Building a Model for Prediction Exchange Rate from USD to VND Using a Novel Method
AU  - Nghien Nguyen Ba
AU  - Cuong Nguyen Thai
AU  - Huyen Le Xuan
AU  - Nhung Nguyen Thi
AU  - Phuong Pham Thi Kim
AU  - Thuy Ngo Thi Bich
Y1  - 2020/10/13
PY  - 2020
N1  - https://doi.org/10.11648/j.ajece.20200402.14
DO  - 10.11648/j.ajece.20200402.14
T2  - American Journal of Electrical and Computer Engineering
JF  - American Journal of Electrical and Computer Engineering
JO  - American Journal of Electrical and Computer Engineering
SP  - 55
EP  - 61
PB  - Science Publishing Group
SN  - 2640-0502
UR  - https://doi.org/10.11648/j.ajece.20200402.14
AB  - 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.
VL  - 4
IS  - 2
ER  -

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Author Information

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

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Plain Text BibTeX RIS

APA Style

Nghien Nguyen Ba, Cuong Nguyen Thai, Huyen Le Xuan, Nhung Nguyen Thi, Phuong Pham Thi Kim, et al. (2020). Building a Model for Prediction Exchange Rate from USD to VND Using a Novel Method. American Journal of Electrical and Computer Engineering, 4(2), 55-61. https://doi.org/10.11648/j.ajece.20200402.14

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ACS Style

Nghien Nguyen Ba; Cuong Nguyen Thai; Huyen Le Xuan; Nhung Nguyen Thi; Phuong Pham Thi Kim, et al. Building a Model for Prediction Exchange Rate from USD to VND Using a Novel Method. Am. J. Electr. Comput. Eng. 2020, 4(2), 55-61. doi: 10.11648/j.ajece.20200402.14

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AMA Style

Nghien Nguyen Ba, Cuong Nguyen Thai, Huyen Le Xuan, Nhung Nguyen Thi, Phuong Pham Thi Kim, et al. Building a Model for Prediction Exchange Rate from USD to VND Using a Novel Method. Am J Electr Comput Eng. 2020;4(2):55-61. doi: 10.11648/j.ajece.20200402.14

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@article{10.11648/j.ajece.20200402.14,
  author = {Nghien Nguyen Ba and Cuong Nguyen Thai and Huyen Le Xuan and Nhung Nguyen Thi and Phuong Pham Thi Kim and Thuy Ngo Thi Bich},
  title = {Building a Model for Prediction Exchange Rate from USD to VND Using a Novel Method},
  journal = {American Journal of Electrical and Computer Engineering},
  volume = {4},
  number = {2},
  pages = {55-61},
  doi = {10.11648/j.ajece.20200402.14},
  url = {https://doi.org/10.11648/j.ajece.20200402.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajece.20200402.14},
  abstract = {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.},
 year = {2020}
}

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TY  - JOUR
T1  - Building a Model for Prediction Exchange Rate from USD to VND Using a Novel Method
AU  - Nghien Nguyen Ba
AU  - Cuong Nguyen Thai
AU  - Huyen Le Xuan
AU  - Nhung Nguyen Thi
AU  - Phuong Pham Thi Kim
AU  - Thuy Ngo Thi Bich
Y1  - 2020/10/13
PY  - 2020
N1  - https://doi.org/10.11648/j.ajece.20200402.14
DO  - 10.11648/j.ajece.20200402.14
T2  - American Journal of Electrical and Computer Engineering
JF  - American Journal of Electrical and Computer Engineering
JO  - American Journal of Electrical and Computer Engineering
SP  - 55
EP  - 61
PB  - Science Publishing Group
SN  - 2640-0502
UR  - https://doi.org/10.11648/j.ajece.20200402.14
AB  - 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.
VL  - 4
IS  - 2
ER  -

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