@inproceedings{e47c1558b0e0478aa0680b56b04335c0,
title = "Stock Market Prediction using Ensemble of Deep Neural Networks",
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.",
keywords = "1DConvNet, CNN, Deep Neural Network, Ensemble Learning, LSTM, Stock Market Prediction",
author = "Chong, {Lu Sin} and Lim, {Kian Ming} and Lee, {Chin Poo}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2020 ; Conference date: 26-09-2020 Through 27-09-2020",
year = "2020",
month = sep,
day = "26",
doi = "10.1109/IICAIET49801.2020.9257864",
language = "English",
series = "IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2020",
}