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...

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Main Authors: Chi Xie, Zhou Mao, Gang-Jin Wang
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
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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
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AT zhoumao forecastingrmbexchangeratebasedonanonlinearcombinationmodelofarfimasvmandbpnn
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