This paper analyzed the development of data mining and the development of the fifth generation (5G) for the Internet of Things\n(IoT) and uses a deep learning method for stock forecasting. In order to solve the problems such as low accuracy and training\ncomplexity caused by complicated data in stock model forecasting, we proposed a forecasting method based on the feature\nselection (FS) and Long Short-Term Memory (LSTM) algorithm to predict the closing price of stock. Considering its future\npotential application, this paper takes 4 stock data from the Shenzhen Component Index as an example and constructs the\nfeature set for prediction based on 17 technical indexes which are commonly used in stock market. The optimal feature set is\ndecided via FS to reduce the dimension of data and the training complexity. The LSTM algorithm is used to forecast closing\nprice of stock. The empirical results show that compared with the LSTM model, the FS-LSTM combination model improves the\naccuracy of prediction and reduces the error between the real value and the forecast value in stock price prediction.
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