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Financial Time Series Forecasting Applying Deep Learning Algorithms

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Information and Communication Technologies (TICEC 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1456))

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Abstract

Deep learning methods can identify and analyze complex patterns and interactions within the data to optimize the trading process. This work presents a deep learning algorithm for intraday stock prices forecasting of Amazon, Inc. We focus on deep architectures such as convolutional neural networks (CNN), long short-term memory (LSTM), and densely-connected neural networks (NN). Results have shown that the combination of these architectures increases the accuracy when forecasting non-stationary time series. Furthermore, the evaluation of the proposed method has resulted in a mean absolute error (MAE) of 6.7 for one-step-ahead forecasting and 9.94 for four-step ahead forecasting.

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Notes

  1. 1.

    https://github.com/ranaroussi/yfinance, Last access: June, 2021.

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Authors and Affiliations

  1. Yachay Tech University, Urcuquí, Ecuador

    Erik Solís, Sherald Noboa & Erick Cuenca

Authors
  1. Erik Solís
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  2. Sherald Noboa
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  3. Erick Cuenca
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Corresponding authors

Correspondence to Erik Solís , Sherald Noboa or Erick Cuenca .

Editor information

Editors and Affiliations

  1. Politecnica Salesiana University, Cuenca, Ecuador

    Juan Pablo Salgado Guerrero

  2. Universidad Técnica Particular de Loja, Loja, Ecuador

    Janneth Chicaiza Espinosa

  3. Politecnica Salesiana University, Cuenca, Ecuador

    Mariela Cerrada Lozada

  4. CEDIA, Cuenca, Ecuador

    Santiago Berrezueta-Guzman

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Solís, E., Noboa, S., Cuenca, E. (2021). Financial Time Series Forecasting Applying Deep Learning Algorithms. In: Salgado Guerrero, J.P., Chicaiza Espinosa, J., Cerrada Lozada, M., Berrezueta-Guzman, S. (eds) Information and Communication Technologies. TICEC 2021. Communications in Computer and Information Science, vol 1456. Springer, Cham. https://doi.org/10.1007/978-3-030-89941-7_4

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  • DOI: https://doi.org/10.1007/978-3-030-89941-7_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-89940-0

  • Online ISBN: 978-3-030-89941-7

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