ISSN 0253-2778

CN 34-1054/N

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Exchange rate prediction method based on ARIMA-HPSO-Elman combined model with SSA： Based on the central parity rate data of USD/CNY

  • Department of Statistics and Finance,School of Management,University of Science and Technology of China, Hefei 230026,China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2020.04.015
  • Received Date: 23 May 2019
  • Accepted Date: 31 August 2019
  • Rev Recd Date: 31 August 2019
  • Publish Date: 30 April 2020
    Abstract

  • Abstract

    Exchange rate has the characteristics of both linear and non-linear mixed behavior. Single linear model and non-linear model are not perfect for forecasting exchange rate.Here the central parity rate series of USD/CNY exchange rate was studied. Firstly, the SSA method was used to denoise the exchange rate series, and ARIMA model was established to fit and predict the reconstructed exchange rate series to extract the linear components of the original exchange rate series. Secondly, the residual part was modeled and predicted by Elman neural network optimized by hybrid particle swarm optimization algorithm based on crossover and mutation. The sum of the results was the predicted value of the original exchange rate series. Empirical results show that CNY exchange rate fluctuation has the characteristics of periodic oscillation. On the 30-day forecast outside the sample of exchange rate series, the performance of the combination model based on SSA method is better than that of the single model and the combination model without SSA method in the short term.

    Abstract

    Exchange rate has the characteristics of both linear and non-linear mixed behavior. Single linear model and non-linear model are not perfect for forecasting exchange rate.Here the central parity rate series of USD/CNY exchange rate was studied. Firstly, the SSA method was used to denoise the exchange rate series, and ARIMA model was established to fit and predict the reconstructed exchange rate series to extract the linear components of the original exchange rate series. Secondly, the residual part was modeled and predicted by Elman neural network optimized by hybrid particle swarm optimization algorithm based on crossover and mutation. The sum of the results was the predicted value of the original exchange rate series. Empirical results show that CNY exchange rate fluctuation has the characteristics of periodic oscillation. On the 30-day forecast outside the sample of exchange rate series, the performance of the combination model based on SSA method is better than that of the single model and the combination model without SSA method in the short term.
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    • References(19)
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    ZHANG Chunlu, BAI Yanping. Application of ARIMA time series and BP neural network combination model in railway passenger rate[J]. Journal of Mathematics in Practice and Theory, 2018, 48(21): 105-113.
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    TSENG F M, YU H C, TZENG G H. Combining neural network model with seasonal time series ARIMA model[J]. Technological Forecasting and Social Change, 2002, 69(1): 71-87.
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    熊志斌. ARIMA融合神经网络的人民币汇率预测模型研究[J]. 数量经济技术经济研究, 2011, (6): 64-76.
    XIONG Zhibin. Research on RMB exchange rate forecasting model based on combining ARIMA with neural networks[J]. The Journal of Quantitative & Technical Economics, 2011, (6): 64-76.
    [17]
    杨进, 陈亮. 基于小波神经网络与ARIMA组合模型在股票预测中的应用[J]. 经济数学, 2018, 35(2): 62-67.
    YANG Jin, CHEN Liang. The application in stock prediction of combination forecast model of wavelet neural network and ARIMA[J]. Journal of Quantitative Economics, 2018, 35(2): 62-67.
    [18]
    ELMAN J L . Finding structure in time[J]. Cognitive Science, 1990, 14(2):179-211.
    [19]
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    [1]
    PLASMANS J, VERKOOIJEN W, DANIELS H. Estimating structural exchange rate models by artificial neural networks[J]. Applied Financial Economics, 1998(8): 541-551.
    [2]
    MAIA A L S, CARVALHO F A T, LUDERMIR T B. Forecasting models for interval-valued time series[J]. Neurocomputing, 2008(71): 3344-3352.
    [3]
    ZHANG G P. Time series forecasting using a hybrid ARIMA and neural network model[J]. Neurocomputing, 2003(50): 159-175.
    [4]
    梁文娟, 李雪艳. 基于ARIMA、LS-SVM和BP神经网络组合模型的航空运输飞行事故征候预测[J]. 安全与环境工程, 2018, 25(1): 130-136.
    LIANG Wenjuan, LI Xueyan. Flight incidents prediction of air transportation based on the combined model of ARIMA, LS-SVM and BPNN[J]. Safety and Environmental Engineering, 2018, 25(1): 130-136.
    [5]
    张春露, 白艳萍. ARIMA时间序列模型和BP神经网络组合预测在铁路客座率中的应用[J]. 数学的实践与认识, 2018, 48(21): 105-113.
    ZHANG Chunlu, BAI Yanping. Application of ARIMA time series and BP neural network combination model in railway passenger rate[J]. Journal of Mathematics in Practice and Theory, 2018, 48(21): 105-113.
    [6]
    孟毅. 时间序列ARIMA与BP神经网络组合模型在CPI预测中的应用[J]. 山东农业大学学报(自然科学版), 2018, 49(6): 1079-1083.
    MENG Yi. Application of ARIMA time series and BP NN combined model in forecast for CPI[J]. Journal of Shandong Agricultural University (Nature Science Edition), 2018, 49(6): 1079-1083.
    [7]
    YU L, WANG S, LAI K K. Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm[J]. Energy Economics, 2008, 30(5): 2623-2635.
    [8]
    谢赤, 郑林林, 孙柏, 等. 基于EMD和Elman网络的人民币汇率时间序列预测[J]. 湖南大学学报（自然科学版）, 2009, 36(6): 89-92.
    XIE Chi, ZHENG Linlin, SUN Bo, et al. Research on the forecasting of RMB exchange rate time series based on EMD and Elman network[J]. Journal of Hunan University (Nature Sciences), 2009, 36(6): 89-92.
    [9]
    王贺, 胡志坚, 陈珍, 等. 基于集合经验模态分解和小波神经网络的短期风功率组合预测[J]. 电工技术学报, 2013, 28(9): 137-144.
    WANG He, HU Zhijian, CHEN Zhen, et al. A hybrid model for wind power forecasting based on ensemble empirical mode decomposition and wavelet neural networks[J]. Transactions of China Electrotechnical Society, 2013, 28(9): 137-144.
    [10]
    ZHOU Q, JIANG H, WANG J, et al. A hybrid model for PM2.5 forecasting based on ensemble empirical mode decomposition and a general regression neural network[J]. Science of the Total Environment, 2014, 496: 264-274.
    [11]
    蒋传进, 宋福根. 基于NARX神经网络与ARMA的汇率混合预测模型[J]. 统计与决策, 2010(15): 33-35.
    [12]
    薛永刚. 基于小波分解的汇率预测模型实证研究[J]. 统计与决策, 2010(20): 125-126.
    [13]
    陈东东, 沐年国. 基于HP滤波分解的ARMA+BPNN的人民币汇率短期预测[J]. 经济研究导刊, 2018(21): 74-77.
    CHEN Dongdong, MU Nianguo. Short-term prediction of RMB exchange rate based on HPA decomposition and ARMA+BPNN[J]. Economic Research Guide, 2018(21): 74-77.
    [14]
    TSENG F M, YU H C, TZENG G H. Combining neural network model with seasonal time series ARIMA model[J]. Technological Forecasting and Social Change, 2002, 69(1): 71-87.
    [15]
    YU L, WANG S, LAI K K. A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates[J]. Computers & Operations Research, 2005, 32(10): 2523-2541.
    [16]
    熊志斌. ARIMA融合神经网络的人民币汇率预测模型研究[J]. 数量经济技术经济研究, 2011, (6): 64-76.
    XIONG Zhibin. Research on RMB exchange rate forecasting model based on combining ARIMA with neural networks[J]. The Journal of Quantitative & Technical Economics, 2011, (6): 64-76.
    [17]
    杨进, 陈亮. 基于小波神经网络与ARIMA组合模型在股票预测中的应用[J]. 经济数学, 2018, 35(2): 62-67.
    YANG Jin, CHEN Liang. The application in stock prediction of combination forecast model of wavelet neural network and ARIMA[J]. Journal of Quantitative Economics, 2018, 35(2): 62-67.
    [18]
    ELMAN J L . Finding structure in time[J]. Cognitive Science, 1990, 14(2):179-211.
    [19]
    张一, 惠晓峰. 基于奇异谱分析的汇率预测研究[J]. 统计与决策, 2012(6): 29-31.)
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