Abstract
Historical data is a permanent collection that allows people to make predictions about the future trends of stocks. With the development of machine learning especially deep learning, the prediction accuracy raised up. In this paper, we proposed feature extraction of stock data through CNN, and a stock prediction algorithm based on deep learning combined with traditional LSTM is constructed. At the same time, the third-order moment is used to improve the traditional stochastic gradient descent, and obtain a dynamic one. Experiments show that compared with other methods it has better accuracy which provides a solution for stock trend prediction by deep learning.
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Li, B., Li, Lf. (2021). Stock Prediction Based on Adaptive Gradient Descent Deep Learning. In: Barolli, L., Woungang, I., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2021. Lecture Notes in Networks and Systems, vol 225. Springer, Cham. https://doi.org/10.1007/978-3-030-75100-5_5
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DOI: https://doi.org/10.1007/978-3-030-75100-5_5
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