Ítem
Solo Metadatos
A three-step deep neural network methodology for exchange rate forecasting
Título de la revista
Autores
Figueroa-García J.C.
LóPez-Santana E.
Franco Franco, Carlos Alberto
Fecha
2017
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Editor
Springer Verlag
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Resumen
Abstract
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. © Springer International Publishing AG 2017.
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Keywords
Computation theory , Finance , Forecasting , Intelligent computing , Time series , Absolute values , Exchange rate forecasting , Exchange rates , Time series forecasting , Deep neural networks
