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Financial Time Series Forecasting via CEEMDAN-LSTM with Exogenous Features

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Intelligent Systems (BRACIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12320))

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Abstract

The most recent successful time series prediction models are a combination of three elements: traditional stochastic models, machine learning models and signal processing techniques. CEEMDAN-LSTM models have combined empirical mode decomposition and long short-term memory neural networks to achieve state-of-the-art results for financial data. In this work, we propose a generalized CEEMDAN-LSTM architecture for time series forecasting capable of dealing with exogenous features as input, and the consequences of input data growth, such as convergence difficulties. Our model was applied to time series from 10 of the most liquid Brazilian stocks, and results show that accuracy is overall improved when compared to the original single feature input CEEMDAN-LSTM architecture..

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Notes

  1. 1.

    https://finance.yahoo.com/quote/PETR4.SA/history?period1=1517961600&period2=1549929600&interval=1d&filter=history&frequency=1d.

  2. 2.

    https://github.com/laszukdawid/PyEMD.

  3. 3.

    https://keras.io/.

  4. 4.

    https://github.com/avilarenan/xlstmceemdan.

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Acknowledgment

The first author thanks BTG Pactual for the support to this work.

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Authors and Affiliations

  1. Computer Engineering Department, Escola Politécnica da Universidade de São Paulo, São Paulo, SP, Brazil

    Renan de Luca Avila & Glauber De Bona

Authors
  1. Renan de Luca Avila
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  2. Glauber De Bona
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Corresponding author

Correspondence to Renan de Luca Avila .

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Editors and Affiliations

  1. Federal University of São Carlos, São Carlos, Brazil

    Ricardo Cerri

  2. Federal University of ABC, Santo Andre, Brazil

    Ronaldo C. Prati

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Both authors declare they have no conflict of interest.

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de Luca Avila, R., De Bona, G. (2020). Financial Time Series Forecasting via CEEMDAN-LSTM with Exogenous Features. In: Cerri, R., Prati, R.C. (eds) Intelligent Systems. BRACIS 2020. Lecture Notes in Computer Science(), vol 12320. Springer, Cham. https://doi.org/10.1007/978-3-030-61380-8_38

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  • DOI: https://doi.org/10.1007/978-3-030-61380-8_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61379-2

  • Online ISBN: 978-3-030-61380-8

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