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Deep Learning for Temporal Stock Prediction: A Comparison

Bohan Xuan
pp.1630-1635 DOI: 10.62051/pg1hn367 August 12, 2024
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Abstract: Deep learning has emerging and numerous applications for most areas, including finance, physics and medical science, etc. The deep-based models achieve satisfying performance on those tasks. In this paper, we aim to provide a summary of deep learning-based temporal stock prediction. Specifically, we first categorize the models into three aspects, including CNN-based models, RNN-based models, and hybrid models, by combining CNN and RNN. Then, we detail the preliminary knowledge for deep-based models, including the components of CNN and RNN. Furthermore, we provide an in-depth review of those methods. Finally, we provide a perspective discussion on the stock prediction tasks for further research. We hope our method can be useful for future research and provide a brief introduction to beginners.
Keywords: Deep Learning; Temporal Stock Prediction; Convolutional Neural Network; Recurrent Neural Network.
APA Citation: Bohan Xuan (2024). Deep Learning for Temporal Stock Prediction: A Comparison. Transactions on Computer Science and Intelligent Systems Research, 5(1), 1630-1635. https://doi.org/10.62051/pg1hn367

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