Stock Market Prediction using Ensemble of Deep Neural Networks

Lu Sin Chong, Kian Ming Lim, Chin Poo Lee

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

22 Citations (Scopus)

Abstract

Stock market prediction has been a challenging task for machine due to time series analysis is needed. In recent years, deep neural networks have been widely applied in many financial time series tasks. Typically, deep neural networks require huge amount of data samples to train a good model. However, the data samples for stock market is limited which caused the networks prone to overfitting. In view of this, this paper leverages deep neural networks with ensemble learning to address this problem. We propose ensemble of Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), and 1DConvNet with LSTM (Conv1DLSTM) to predict the stock market price, named EnsembleDNNs. The performance of the proposed EnsembleDNNs is evaluated with stock market of several companies. The experiment results show encouraging performance as compared to other baselines.

Original languageEnglish
Title of host publicationIEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728169460
DOIs
Publication statusPublished - 26 Sept 2020
Externally publishedYes
Event2020 IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2020 - Kota Kinabalu, Sabah, Malaysia
Duration: 26 Sept 202027 Sept 2020

Publication series

NameIEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2020

Conference

Conference2020 IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2020
Country/TerritoryMalaysia
CityKota Kinabalu, Sabah
Period26/09/2027/09/20

Keywords

  • 1DConvNet
  • CNN
  • Deep Neural Network
  • Ensemble Learning
  • LSTM
  • Stock Market Prediction

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Information Systems and Management
  • Instrumentation

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