Abstract
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.
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This research is supported by Shandong Provincial Natural Science Foundation (ZR201911150391).
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Wu, J.MT., Li, Z., Djenouri, Y., Polap, D., Srivastava, G., Lin, J.CW. (2021). A Graphic CNN-LSTM Model for Stock Price Predication. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2021. Lecture Notes in Computer Science(), vol 12854. Springer, Cham. https://doi.org/10.1007/978-3-030-87986-0_23
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