Stock Market Prediction using Ensemble of Deep Neural Networks


Chong, Lu Sin and Lim, Kian Ming and Lee, Chin Poo (2020) Stock Market Prediction using Ensemble of Deep Neural Networks. In: 2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET). IEEE, pp. 1-5. ISBN 978-1-7281-6946-0

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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.

Item Type: Book Section
Uncontrolled Keywords: Stock exchanges, Stock markets, Logic gates, Training, Testing, Neural networks, Genetic algorithms, Recurrent neural networks
Subjects: H Social Sciences > HG Finance > HG4501-6051 Investment, capital formation, speculation > HG4551-4598 Stock exchanges
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 24 Jan 2021 04:03
Last Modified: 24 Jan 2021 04:08


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