Financial Forecasting via Deep-Learning and Machine-Learning Tools over Two-Dimensional Objects Transformed from Time Series

• Alessandro Baldo ^[4] ; Alfredo Cuzzocrea ^{[1] [5]} ; Edoardo Fadda ^{[4] [2]} ; Pablo G. Bringas ^[3]
  1. [1] University of Calabria

     University of Calabria

     Cosenza, Italia

     
  2. [2] Polytechnic University of Turin

     Polytechnic University of Turin

     Torino, Italia

     
  3. [3] Universidad de Deusto

     Universidad de Deusto

     Bilbao, España

     
  4. [4] ISIRES (Torino, Italy)
  5. [5] LORIA (Nancy, France)

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• Localización: Hybrid Artificial Intelligent Systems: 16th International Conference, HAIS 2021. Bilbao, Spain. September 22–24, 2021. Proceedings / coord. por Hugo Sanjurjo González, Iker Pastor López, Pablo García Bringas, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2021, ISBN 978-3-030-86271-8, págs. 550-563
• Idioma: inglés
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• Resumen

  • In this study, we propose a deeper analysis on the algorithmic treatment of financial time series, with a focus on Forex markets’ applications. The relevant aspects of the paper refers to a more beneficial data arrangement, proposed into a two-dimensional objects and to the application of a Temporal Convolutional Neural Network model, representing a more than valid alternative to Recurrent Neural Networks. The results are supported by expanding the comparison to other more consolidated deep learning models, as well as with some of the most performing Machine Learning methods. Finally, a financial framework is proposed to test the real effectiveness of the algorithms.