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

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Book cover Intelligent Computing Theories and Application (ICIC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10361))

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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.

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Author information

Authors and Affiliations

  1. Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Juan Carlos Figueroa-García & Eduyn López-Santana

  2. Management Department, Universidad del Rosario, Bogotá, Colombia

    Carlos Franco-Franco

Authors
  1. Juan Carlos Figueroa-García
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  2. Eduyn López-Santana
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  3. Carlos Franco-Franco
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Corresponding author

Correspondence to Juan Carlos Figueroa-García .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Politecnico di Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

  4. Indian Institute of Technology, Kanpur, India

    Phalguni Gupta

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Figueroa-García, J.C., López-Santana, E., Franco-Franco, C. (2017). A Three-Step Deep Neural Network Methodology for Exchange Rate Forecasting. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_70

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  • DOI: https://doi.org/10.1007/978-3-319-63309-1_70

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63308-4

  • Online ISBN: 978-3-319-63309-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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