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Stacked Deep Learning Structure with Bidirectional Long-Short Term Memory for Stock Market Prediction

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Neural Computing for Advanced Applications (NCAA 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1265))

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Abstract

The rapid growth of deep learning research has introduced numerous methods to solve real-world applications. In the financial market, the stock price prediction is one of the most challenging topics. This paper presents design and implementation of a stacked system to predict the stock price of the next day. This approach is a method that considers the historical data of the real stock prices from Yahoo Finance. This model uses the wavelet transform technique to reduce the noise of market data, and stacked auto-encoder to filter unimportant features from preprocessed data. Finally, recurrent neural network and bidirectional long-short term memory are used to predict the future stock price. We evaluate our model by analyzing the performance of different models with time series evaluation criteria.

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Author information

Authors and Affiliations

  1. College of Computer Science and Electronics Engineering, Hunan University, Changsha, China

    Ying Xu, Lymeng Chhim & Bingxin Zheng

  2. Graduate School of Engineering, Osaka Prefecture University, Osaka, 599-8531, Japan

    Lymeng Chhim, Bingxin Zheng & Yusuke Nojima

Authors
  1. Ying Xu
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  2. Lymeng Chhim
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  3. Bingxin Zheng
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  4. Yusuke Nojima
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Corresponding author

Correspondence to Ying Xu .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Haijun Zhang

  2. Hefei University of Technology, Hefei, China

    Zhao Zhang

  3. Chongqing University, Chongqing, China

    Zhou Wu

  4. South China Normal University, Guangzhou, China

    Tianyong Hao

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Xu, Y., Chhim, L., Zheng, B., Nojima, Y. (2020). Stacked Deep Learning Structure with Bidirectional Long-Short Term Memory for Stock Market Prediction. In: Zhang, H., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2020. Communications in Computer and Information Science, vol 1265. Springer, Singapore. https://doi.org/10.1007/978-981-15-7670-6_37

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  • DOI: https://doi.org/10.1007/978-981-15-7670-6_37

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-7669-0

  • Online ISBN: 978-981-15-7670-6

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