利用神经网络预测汇率及弗里德曼检验的结果

Q4 Mathematics
Dr. N. Konda Reddy, Dr. K Murali Krishna
{"title":"利用神经网络预测汇率及弗里德曼检验的结果","authors":"Dr. N. Konda Reddy, Dr. K Murali Krishna","doi":"10.52783/cana.v31.1051","DOIUrl":null,"url":null,"abstract":"Forecasting of exchange rates plays a pivotal role in global trade, stocks and making the policies of exports and imports. USD exchange rates used widely for many business areas. In this paper an attempt is made to predict INR/USD exchange rates using Feed forward Neural Networks and Box-Jenkins methodology. The forecasting performance of the developed models were tested using error measures like MAE, MAPE and RMSE. The results shows FFNN model has better model than ARIMA model. The predicted exchange rates would vary between 83.06 and 83.28 for the out sample and this variation is exchange rates would help the business people and also for framing the government policies in the future. ","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":" 46","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of Exchange Rates using Neural Networks and Performance by Friedman’s Test\",\"authors\":\"Dr. N. Konda Reddy, Dr. K Murali Krishna\",\"doi\":\"10.52783/cana.v31.1051\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Forecasting of exchange rates plays a pivotal role in global trade, stocks and making the policies of exports and imports. USD exchange rates used widely for many business areas. In this paper an attempt is made to predict INR/USD exchange rates using Feed forward Neural Networks and Box-Jenkins methodology. The forecasting performance of the developed models were tested using error measures like MAE, MAPE and RMSE. The results shows FFNN model has better model than ARIMA model. The predicted exchange rates would vary between 83.06 and 83.28 for the out sample and this variation is exchange rates would help the business people and also for framing the government policies in the future. \",\"PeriodicalId\":40036,\"journal\":{\"name\":\"Communications on Applied Nonlinear Analysis\",\"volume\":\" 46\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Communications on Applied Nonlinear Analysis\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.52783/cana.v31.1051\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Mathematics\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Communications on Applied Nonlinear Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.52783/cana.v31.1051","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Mathematics","Score":null,"Total":0}
引用次数: 0

摘要

汇率预测在全球贸易、股票和进出口政策制定中起着举足轻重的作用。美元汇率被广泛应用于许多商业领域。本文尝试使用前馈神经网络和 Box-Jenkins 方法预测印度卢比/美元汇率。使用 MAE、MAPE 和 RMSE 等误差指标测试了所开发模型的预测性能。结果显示,FFNN 模型比 ARIMA 模型更好。样本外的预测汇率将在 83.06 和 83.28 之间变化,这种汇率变化将有助于商业人士和政府未来政策的制定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Exchange Rates using Neural Networks and Performance by Friedman’s Test
Forecasting of exchange rates plays a pivotal role in global trade, stocks and making the policies of exports and imports. USD exchange rates used widely for many business areas. In this paper an attempt is made to predict INR/USD exchange rates using Feed forward Neural Networks and Box-Jenkins methodology. The forecasting performance of the developed models were tested using error measures like MAE, MAPE and RMSE. The results shows FFNN model has better model than ARIMA model. The predicted exchange rates would vary between 83.06 and 83.28 for the out sample and this variation is exchange rates would help the business people and also for framing the government policies in the future. 
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
0.30
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信