Forecasting chaotic time series of exchange rate based on nonlinear autoregressive model

摘要：

Exchange rate time series is often characterized as chaotic in nature. The prediction using conventional statistical techniques and neural network with back propagation algorithm, which is most widely applied, do not give reliable prediction results. Exchangerate time series is also a dynamic non-linear system, whose characteristics cannot be reflected by the static neutral network. The Nonlinear Autoregressive with exogenous input (NARX) includes the feedback of the network output, therefore can reflect the dynamic property of the system. This paper proved the chaotic property of the exchange-rate time series, calculated the embedding dimension and time delay of the series, and established the exchange-rate forecast model using the NARX network. The result shows that the NARX network has better short-term forecast effect, comparing to the BP network and the SVM model.

关键词： Chao Nonlinear autoregressive model NARX exchange rate

作者: Chuanjin Jiang Fugen Song

作者单位: Shanghai Business School Donghua University Shanghai,China Donghua University Shanghai,China

会议类型: 国际会议

会议名称: The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

会议地点: 沈阳

会议语种:英文

页码: 238-241

在线出版日期: 2010-03-27（万方平台首次上网日期，不代表论文的发表时间）