TY - GEN
T1 - A Graphic CNN-LSTM Model for Stock Price Predication
AU - Wu, Jimmy Ming Tai
AU - Li, Zhongcui
AU - Djenouri, Youcef
AU - Polap, Dawid
AU - Srivastava, Gautam
AU - Lin, Jerry Chun Wei
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - In this paper, we presented a novel model that combines Convolution Neural Network (CNN) and Long Short-term Memory Neural Network (LSTM) for better and accurate stock price prediction. We then developed a model called stock sequence array convolutional LSTM (SACLSTM) that builds both a sequence array of the historical data and leading indicators (i.e., futures and options). This built array is then considered as the input data of the CNN model, thus specific feature vectors via convolutional and pooling layers are then extracted for being the input vector of the LSTM model. Based on this flowchart, the stock price can be better predicted, that can be seen from the conducted experiments in 10 stocks data from USA and Taiwan stock markets. Results also indicated that the designed model is better than the existing models.
AB - In this paper, we presented a novel model that combines Convolution Neural Network (CNN) and Long Short-term Memory Neural Network (LSTM) for better and accurate stock price prediction. We then developed a model called stock sequence array convolutional LSTM (SACLSTM) that builds both a sequence array of the historical data and leading indicators (i.e., futures and options). This built array is then considered as the input data of the CNN model, thus specific feature vectors via convolutional and pooling layers are then extracted for being the input vector of the LSTM model. Based on this flowchart, the stock price can be better predicted, that can be seen from the conducted experiments in 10 stocks data from USA and Taiwan stock markets. Results also indicated that the designed model is better than the existing models.
KW - Convolution neural network
KW - Leading indicators
KW - Long short-term memory neural network
KW - Stock price prediction
UR - https://www.scopus.com/pages/publications/85117460061
U2 - 10.1007/978-3-030-87986-0_23
DO - 10.1007/978-3-030-87986-0_23
M3 - Conference contribution
AN - SCOPUS:85117460061
SN - 9783030879853
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 258
EP - 268
BT - Artificial Intelligence and Soft Computing - 20th International Conference, ICAISC 2021, Proceedings
A2 - Rutkowski, Leszek
A2 - Scherer, Rafał
A2 - Korytkowski, Marcin
A2 - Pedrycz, Witold
A2 - Tadeusiewicz, Ryszard
A2 - Zurada, Jacek M.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 20th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2021
Y2 - 21 June 2021 through 23 June 2021
ER -