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Stock Price Prediction Using Time Convolution Long Short-Term Memory Network

  • Xukuan Zhan
  • Yuhua Li
  • Ruixuan Li
  • Xiwu Gu
  • Olivier Habimana
  • Haozhao Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11061)

Abstract

The time series of stock prices are non-stationary and non-linear, making the prediction of future price trends much challenging. Inspired by Convolutional Neural Network (CNN), we make convolution on the time dimension to capture the long-term fluctuation features of stock series. To learn long-term dependencies of stock prices, we combine the time convolution with Long Short-Term Memory (LSTM), and propose a novel deep learning model named Time Convolution Long Short-Term Memory (TC-LSTM) networks. TC-LSTM can obtain the stock longer data dependence and overall change pattern. The experiments on two real market datasets demonstrate that the proposed model outperforms other three baseline models in the mean square error.

Keywords

Time convolution Long Short-Term Memory (LSTM) Stock price prediction 

Notes

Acknowledgments

This work is supported by the National Key Research and Development Program of China under grants 2016QY01W0202 and 2016TFB0800402, the National Natural Science Foundation of China under grants 61572221, U1401258, 61433006 and 61502185, Guangxi High level innovation Team in Higher Education Institutions–Innovation Team of ASEAN Digital Cloud Big Data Security and Mining Technology.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Xukuan Zhan
    • 1
  • Yuhua Li
    • 1
  • Ruixuan Li
    • 1
  • Xiwu Gu
    • 1
  • Olivier Habimana
    • 1
  • Haozhao Wang
    • 1
  1. 1.School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina

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