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http://dx.doi.org/10.17661/jkiiect.2018.11.2.204

Stock Prediction Model based on Bidirectional LSTM Recurrent Neural Network  

Joo, Il-Taeck (Department of Computer Science, Dongshin University)
Choi, Seung-Ho (Department of Computer Science, Dongshin University)
Publication Information
The Journal of Korea Institute of Information, Electronics, and Communication Technology / v.11, no.2, 2018 , pp. 204-208 More about this Journal
Abstract
In this paper, we proposed and evaluated the time series deep learning prediction model for learning fluctuation pattern of stock price. Recurrent neural networks, which can store previous information in the hidden layer, are suitable for the stock price prediction model, which is time series data. In order to maintain the long - term dependency by solving the gradient vanish problem in the recurrent neural network, we use LSTM with small memory inside the recurrent neural network. Furthermore, we proposed the stock price prediction model using bidirectional LSTM recurrent neural network in which the hidden layer is added in the reverse direction of the data flow for solving the limitation of the tendency of learning only based on the immediately preceding pattern of the recurrent neural network. In this experiment, we used the Tensorflow to learn the proposed stock price prediction model with stock price and trading volume input. In order to evaluate the performance of the stock price prediction, the mean square root error between the real stock price and the predicted stock price was obtained. As a result, the stock price prediction model using bidirectional LSTM recurrent neural network has improved prediction accuracy compared with unidirectional LSTM recurrent neural network.
Keywords
Bidirectional; Deep learning; LSTM; Long Short-Term Memory; Prediction; Recurrent Neural Network(RNN); Stock Price;
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