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

Carlos Alberto Franco Franco, Juan Carlos Figueroa-Garcia, eduyn ramiro Lopéz-Santana

Resultado de la investigación: Contribución a RevistaArtículo

Resumen

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.
Idioma originalEnglish (US)
Páginas (desde-hasta)786-795
Número de páginas10
PublicaciónLecture Notes in Computer Science
Volumen10361
EstadoPublished - 2017

Huella dactilar

Volatiles
Exchange rate
Forecasting
Time series
Neural Networks
Time Series Forecasting
Methodology
Absolute value
Forecast
Nonlinearity
Deep neural networks
Trends
Learning
Deep learning

Citar esto

Franco Franco, Carlos Alberto ; Figueroa-Garcia, Juan Carlos ; Lopéz-Santana, eduyn ramiro. / A Three-Step Deep Neural Network Methodology for Exchange Rate Forecasting. En: Lecture Notes in Computer Science. 2017 ; Vol. 10361. pp. 786-795.
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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.

En: Lecture Notes in Computer Science, Vol. 10361, 2017, p. 786-795.

Resultado de la investigación: Contribución a RevistaArtículo

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