A Three-Step Deep Neural Network Methodology for Exchange Rate Forecasting

被引：0

作者：

Carlos Figueroa-Garcia, Juan ^{[1
]}

Lopez-Santana, Eduyn ^{[1
]}

Franco-Franco, Carlos ^{[2
]}

机构：

[1] Univ Dist Francisco Jose de Caldas, Bogota, Colombia

[2] Univ Rosario, Management Dept, Bogota, Colombia

来源：

INTELLIGENT COMPUTING THEORIES AND APPLICATION, ICIC 2017, PT I | 2017年 / 10361卷

关键词：

D O I：

10.1007/978-3-319-63309-1_70

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a methodology for volatile time series forecasting using deep learning. We use a three-step methodology in order to remove trend and nonlinearities from data before applying two parallel deep neural networks to forecast two main features from processed data: absolute value and sign. The proposal is successfully applied to a volatile exchange rate time series problem.

引用

页码：786 / 795

页数：10

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