FOREX Rate Prediction using Chaos and Quantile Regression Random Forest

被引：0
|
作者：
Pradeepkumar, Dadabada [1 ,2 ]
Ravi, Vadlamani [2 ]
机构：
[1] Univ Hyderabad, SCIS, Hyderabad 500046, Andhra Pradesh, India
[2] Inst Dev & Res Banking Technol, Ctr Excellence Analyt, Castle Hills Rd 1, Hyderabad 500057, Andhra Pradesh, India
来源：
2016 3RD INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN INFORMATION TECHNOLOGY (RAIT) | 2016年
关键词：
FOREX Rate Prediction; Hybrid model; chaos; QR; RF; QRRF;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents a hybrid of chaos modeling and Quantile Regression Random Forest (QRRF) for Foreign Exchange (FOREX) Rate prediction. The exchange rates data of US Dollar (USD) versus Japanese Yen (JPY), British Pound (GBP), and Euro (EUR) are used to test the efficacy of proposed model. Based on the experiments conducted, we conclude that the proposed model yielded accurate predictions compared to Chaos + Quantile Regression (QR), Chaos+Random Forest (RF) and that of Pradeepkumar and Ravi [12] in terms of both Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE).
引用
收藏
页码：517 / 522
页数：6
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