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

中文翻译：

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基于5G物联网的股票预测模型FS-LSTM

本文分析了数据挖掘的发展以及物联网（IoT）的第五代（5G）的发展，并将深度学习方法用于股票预测。针对股票模型预测中复杂数据导致准确性低，训练复杂度低的问题，提出了一种基于特征选择（FS）和长短期记忆（LSTM）算法的收盘价预测方法。库存。考虑到其未来的潜在应用，本文以深圳成分指数中的4个股票数据为例，并基于股票市场常用的17个技术指标构建了预测特征集。最佳功能集是通过FS确定的，以减少数据量和训练复杂度。LSTM算法用于预测股票的收盘价。实证结果表明，与LSTM模型相比，FS-LSTM组合模型提高了预测的准确性，并减少了股价预测中真实值与预测值之间的误差。