{"title":"基于动态BP神经网络模型和ARMA模型的人民币汇率预测精度比较","authors":"Zhiqiang Ye, Xiang Ren, Yaling Shan","doi":"10.4172/2168-9458.1000161","DOIUrl":null,"url":null,"abstract":"This paper uses the dynamic back propagation (BP) neural network model and the autoregressive moving average (ARMA) model to forecast the RMB exchange rate based on the data from January 1, 2011 to October 10, 2012. The results show that the dynamic BP neural network model works better than the ARMA model in evaluating both the trend and the deviation of RMB exchange rate.","PeriodicalId":315937,"journal":{"name":"Journal of Stock & Forex Trading","volume":"66 6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Comparing RMB Exchange Rate Forecasting Accuracy based on Dynamic BP Neural Network Model and the ARMA Model\",\"authors\":\"Zhiqiang Ye, Xiang Ren, Yaling Shan\",\"doi\":\"10.4172/2168-9458.1000161\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper uses the dynamic back propagation (BP) neural network model and the autoregressive moving average (ARMA) model to forecast the RMB exchange rate based on the data from January 1, 2011 to October 10, 2012. The results show that the dynamic BP neural network model works better than the ARMA model in evaluating both the trend and the deviation of RMB exchange rate.\",\"PeriodicalId\":315937,\"journal\":{\"name\":\"Journal of Stock & Forex Trading\",\"volume\":\"66 6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Stock & Forex Trading\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4172/2168-9458.1000161\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Stock & Forex Trading","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4172/2168-9458.1000161","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Comparing RMB Exchange Rate Forecasting Accuracy based on Dynamic BP Neural Network Model and the ARMA Model
This paper uses the dynamic back propagation (BP) neural network model and the autoregressive moving average (ARMA) model to forecast the RMB exchange rate based on the data from January 1, 2011 to October 10, 2012. The results show that the dynamic BP neural network model works better than the ARMA model in evaluating both the trend and the deviation of RMB exchange rate.
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