It focuses on the problems of forecasting exchange rate that is a nonlinear time series. A dynamics systems approach and the recurrent neural networks (RNN) were employed to modeling this nonlinear time series. The delay time was calculated using mutual information method and embedding dimension was confirmed by false nearest neighbors. The dataset was reconstructed form source time series for trained and verified the neural networks model. The quadratic optimization criterion was considered which the neural networks weights update algorithm were derived using gradient descent method for hidden layer; recurrent layer and output layer. The calculation flow chart was designed for neural networks learning and emulation. The reliability and stability of neural networks was confirmed by testing dataset. The results of simulation showed that the recurrent neural networks were preferably performance for prediction the change of exchange rate.
| Published in | International Journal of Economics, Finance and Management Sciences (Volume 5, Issue 6) |
| DOI | 10.11648/j.ijefm.20170506.14 |
| Page(s) | 300-303 |
| 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), 2017. Published by Science Publishing Group |
Neural Networks, Forecasting Exchange Rate, Nonlinear Time Series
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APA Style
Yuxi Ye. (2017). Study on Exchange Rate Forecasting Using Recurrent Neural Networks. International Journal of Economics, Finance and Management Sciences, 5(6), 300-303. https://doi.org/10.11648/j.ijefm.20170506.14
ACS Style
Yuxi Ye. Study on Exchange Rate Forecasting Using Recurrent Neural Networks. Int. J. Econ. Finance Manag. Sci. 2017, 5(6), 300-303. doi: 10.11648/j.ijefm.20170506.14
AMA Style
Yuxi Ye. Study on Exchange Rate Forecasting Using Recurrent Neural Networks. Int J Econ Finance Manag Sci. 2017;5(6):300-303. doi: 10.11648/j.ijefm.20170506.14
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Download@article{10.11648/j.ijefm.20170506.14,
author = {Yuxi Ye},
title = {Study on Exchange Rate Forecasting Using Recurrent Neural Networks},
journal = {International Journal of Economics, Finance and Management Sciences},
volume = {5},
number = {6},
pages = {300-303},
doi = {10.11648/j.ijefm.20170506.14},
url = {https://doi.org/10.11648/j.ijefm.20170506.14},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijefm.20170506.14},
abstract = {It focuses on the problems of forecasting exchange rate that is a nonlinear time series. A dynamics systems approach and the recurrent neural networks (RNN) were employed to modeling this nonlinear time series. The delay time was calculated using mutual information method and embedding dimension was confirmed by false nearest neighbors. The dataset was reconstructed form source time series for trained and verified the neural networks model. The quadratic optimization criterion was considered which the neural networks weights update algorithm were derived using gradient descent method for hidden layer; recurrent layer and output layer. The calculation flow chart was designed for neural networks learning and emulation. The reliability and stability of neural networks was confirmed by testing dataset. The results of simulation showed that the recurrent neural networks were preferably performance for prediction the change of exchange rate.},
year = {2017}
}
TY - JOUR T1 - Study on Exchange Rate Forecasting Using Recurrent Neural Networks AU - Yuxi Ye Y1 - 2017/11/20 PY - 2017 N1 - https://doi.org/10.11648/j.ijefm.20170506.14 DO - 10.11648/j.ijefm.20170506.14 T2 - International Journal of Economics, Finance and Management Sciences JF - International Journal of Economics, Finance and Management Sciences JO - International Journal of Economics, Finance and Management Sciences SP - 300 EP - 303 PB - Science Publishing Group SN - 2326-9561 UR - https://doi.org/10.11648/j.ijefm.20170506.14 AB - It focuses on the problems of forecasting exchange rate that is a nonlinear time series. A dynamics systems approach and the recurrent neural networks (RNN) were employed to modeling this nonlinear time series. The delay time was calculated using mutual information method and embedding dimension was confirmed by false nearest neighbors. The dataset was reconstructed form source time series for trained and verified the neural networks model. The quadratic optimization criterion was considered which the neural networks weights update algorithm were derived using gradient descent method for hidden layer; recurrent layer and output layer. The calculation flow chart was designed for neural networks learning and emulation. The reliability and stability of neural networks was confirmed by testing dataset. The results of simulation showed that the recurrent neural networks were preferably performance for prediction the change of exchange rate. VL - 5 IS - 6 ER -
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