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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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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