Forecasting RMB Exchange Rate Based on a Nonlinear Combination Model of ARFIMA, SVM, and BPNN
There are various models to predict financial time series like the RMB exchange rate. In this paper, considering the complex characteristics of RMB exchange rate, we build a nonlinear combination model of the autoregressive fractionally integrated moving average (ARFIMA) model, the support vector ma...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2015-01-01
|
Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2015/635345 |
id |
doaj-e8ce8c1aec4c48959784e2c6bbfaafd2 |
---|---|
record_format |
Article |
spelling |
doaj-e8ce8c1aec4c48959784e2c6bbfaafd22020-11-24T22:28:44ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472015-01-01201510.1155/2015/635345635345Forecasting RMB Exchange Rate Based on a Nonlinear Combination Model of ARFIMA, SVM, and BPNNChi Xie0Zhou Mao1Gang-Jin Wang2College of Business Administration, Hunan University, Changsha 410082, ChinaCollege of Business Administration, Hunan University, Changsha 410082, ChinaCollege of Business Administration, Hunan University, Changsha 410082, ChinaThere are various models to predict financial time series like the RMB exchange rate. In this paper, considering the complex characteristics of RMB exchange rate, we build a nonlinear combination model of the autoregressive fractionally integrated moving average (ARFIMA) model, the support vector machine (SVM) model, and the back-propagation neural network (BPNN) model to forecast the RMB exchange rate. The basic idea of the nonlinear combination model (NCM) is to make the prediction more effective by combining different models’ advantages, and the weight of the combination model is determined by a nonlinear weighted mechanism. The RMB exchange rate against US dollar (RMB/USD) and the RMB exchange rate against Euro (RMB/EUR) are used as the empirical examples to evaluate the performance of NCM. The results show that the prediction performance of the nonlinear combination model is better than the single models and the linear combination models, and the nonlinear combination model is suitable for the prediction of the special time series, such as the RMB exchange rate.http://dx.doi.org/10.1155/2015/635345 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Chi Xie Zhou Mao Gang-Jin Wang |
spellingShingle |
Chi Xie Zhou Mao Gang-Jin Wang Forecasting RMB Exchange Rate Based on a Nonlinear Combination Model of ARFIMA, SVM, and BPNN Mathematical Problems in Engineering |
author_facet |
Chi Xie Zhou Mao Gang-Jin Wang |
author_sort |
Chi Xie |
title |
Forecasting RMB Exchange Rate Based on a Nonlinear Combination Model of ARFIMA, SVM, and BPNN |
title_short |
Forecasting RMB Exchange Rate Based on a Nonlinear Combination Model of ARFIMA, SVM, and BPNN |
title_full |
Forecasting RMB Exchange Rate Based on a Nonlinear Combination Model of ARFIMA, SVM, and BPNN |
title_fullStr |
Forecasting RMB Exchange Rate Based on a Nonlinear Combination Model of ARFIMA, SVM, and BPNN |
title_full_unstemmed |
Forecasting RMB Exchange Rate Based on a Nonlinear Combination Model of ARFIMA, SVM, and BPNN |
title_sort |
forecasting rmb exchange rate based on a nonlinear combination model of arfima, svm, and bpnn |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2015-01-01 |
description |
There are various models to predict financial time series like the RMB exchange rate. In this paper, considering the complex characteristics of RMB exchange rate, we build a nonlinear combination model of the autoregressive fractionally integrated moving average (ARFIMA) model, the support vector machine (SVM) model, and the back-propagation neural network (BPNN) model to forecast the RMB exchange rate. The basic idea of the nonlinear combination model (NCM) is to make the prediction more effective by combining different models’ advantages, and the weight of the combination model is determined by a nonlinear weighted mechanism. The RMB exchange rate against US dollar (RMB/USD) and the RMB exchange rate against Euro (RMB/EUR) are used as the empirical examples to evaluate the performance of NCM. The results show that the prediction performance of the nonlinear combination model is better than the single models and the linear combination models, and the nonlinear combination model is suitable for the prediction of the special time series, such as the RMB exchange rate. |
url |
http://dx.doi.org/10.1155/2015/635345 |
work_keys_str_mv |
AT chixie forecastingrmbexchangeratebasedonanonlinearcombinationmodelofarfimasvmandbpnn AT zhoumao forecastingrmbexchangeratebasedonanonlinearcombinationmodelofarfimasvmandbpnn AT gangjinwang forecastingrmbexchangeratebasedonanonlinearcombinationmodelofarfimasvmandbpnn |
_version_ |
1725746457523257344 |