A three-step deep neural network methodology for exchange rate forecasting

Juan Carlos Figueroa-García, Eduyn LóPez-Santana, Carlos Franco-Franco

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationIntelligent Computing Theories and Application - 13th International Conference, ICIC 2017, Proceedings
EditorsDe-Shuang Huang, Prashan Premaratne, Vitoantonio Bevilacqua, Phalguni Gupta
PublisherSpringer
Pages786-795
Number of pages10
ISBN (Print)9783319633084
DOIs
StatePublished - 2017
Event13th International Conference on Intelligent Computing, ICIC 2017 - Liverpool, United Kingdom
Duration: Aug 7 2017Aug 10 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10361 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Conference on Intelligent Computing, ICIC 2017
CountryUnited Kingdom
CityLiverpool
Period8/7/178/10/17

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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