Abstract
Original language | English (US) |
---|---|
Pages (from-to) | 786-795 |
Number of pages | 10 |
Journal | Lecture Notes in Computer Science |
Volume | 10361 |
State | Published - 2017 |
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A Three-Step Deep Neural Network Methodology for Exchange Rate Forecasting. / Franco Franco, Carlos Alberto; Figueroa-Garcia, Juan Carlos ; Lopéz-Santana, eduyn ramiro.
In: Lecture Notes in Computer Science, Vol. 10361, 2017, p. 786-795.Research output: Contribution to journal › Article
TY - JOUR
T1 - A Three-Step Deep Neural Network Methodology for Exchange Rate Forecasting
AU - Franco Franco, Carlos Alberto
AU - Figueroa-Garcia, Juan Carlos
AU - Lopéz-Santana, eduyn ramiro
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
M3 - Article
VL - 10361
SP - 786
EP - 795
JO - Lecture Notes in Computer Science
JF - Lecture Notes in Computer Science
SN - 0302-9743
ER -