TY - GEN
T1 - A three-step deep neural network methodology for exchange rate forecasting
AU - Figueroa-García, Juan Carlos
AU - LóPez-Santana, Eduyn
AU - Franco-Franco, Carlos
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
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.
UR - http://www.scopus.com/inward/record.url?scp=85027707850&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85027707850&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-63309-1_70
DO - 10.1007/978-3-319-63309-1_70
M3 - Conference contribution
AN - SCOPUS:85027707850
SN - 9783319633084
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 786
EP - 795
BT - Intelligent Computing Theories and Application - 13th International Conference, ICIC 2017, Proceedings
A2 - Huang, De-Shuang
A2 - Premaratne, Prashan
A2 - Bevilacqua, Vitoantonio
A2 - Gupta, Phalguni
PB - Springer
T2 - 13th International Conference on Intelligent Computing, ICIC 2017
Y2 - 7 August 2017 through 10 August 2017
ER -