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

  1. Alessandro Baldo 2
  2. Alfredo Cuzzocrea 13
  3. Edoardo Fadda 24
  4. Pablo G. Bringas 5
  1. 1 LORIA (Nancy, France)
  2. 2 ISIRES (Torino, Italy)
  3. 3 University of Calabria
    info

    University of Calabria

    Cosenza, Italia

    ROR https://ror.org/02rc97e94

  4. 4 Polytechnic University of Turin
    info

    Polytechnic University of Turin

    Turín, Italia

    ROR https://ror.org/00bgk9508

  5. 5 Universidad de Deusto
    info

    Universidad de Deusto

    Bilbao, España

    ROR https://ror.org/00ne6sr39

Erakutsi afiliazioak +
Liburua:
Hybrid Artificial Intelligent Systems: 16th International Conference, HAIS 2021. Bilbao, Spain. September 22–24, 2021. Proceedings
  1. Hugo Sanjurjo González (coord.)
  2. Iker Pastor López (coord.)
  3. Pablo García Bringas (coord.)
  4. Héctor Quintián (coord.)
  5. Emilio Corchado (coord.)

Argitaletxea: Springer International Publishing AG

ISBN: 978-3-030-86271-8,  978-3-030-86270-1

Argitalpen urtea: 2021

Orrialdeak: 550-563

Biltzarra: Hybrid Artificial Intelligent Systems (HAIS) (16. 2021. Bilbao)

Mota: Biltzar ekarpena

DIALNET GOOGLE SCHOLAR

Laburpena

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.

Datuen iturria: Dialnet