In recent years, with the breakthrough of big data and deep learning technology in various fields, many scholars have begun to study the stock market time series by using deep learning technology. In the process of model training, the selection of training samples, model structure and optimization methods are often subjective. Therefore, studying these influencing factors is beneficial to provide scientific suggestions for the training of recurrent neural networks and is beneficial to improve the prediction accuracy of the model. In this paper, the LSTM deep neural network is used to model and predict the financial transaction data of Shanghai, and the three types of factors affecting the prediction accuracy of the model are systematically studied. Finally, a high-precision short-term prediction model of financial market time series based on LSTM deep neural network is constructed. In addition, this paper compares BP neural network, traditional RNN and RNN improved LSTM deep neural network. It proves that the LSTM deep neural network has higher prediction accuracy and can effectively predict the stock market time series.

近年来，随着大数据和深度学习技术在各个领域的突破，许多学者开始使用深度学习技术研究股票市场时间序列。在模型训练过程中，训练样本的选择，模型结构和优化方法通常是主观的。因此，研究这些影响因素有利于为递归神经网络的训练提供科学的建议，并有助于提高模型的预测精度。本文采用LSTM深度神经网络对上海的金融交易数据进行建模和预测，系统地研究了影响模型预测准确性的三类因素。最后，建立了基于LSTM深度神经网络的高精度金融市场时间序列短期预测模型。另外，本文比较了BP神经网络，传统RNN和RNN改进的LSTM深度神经网络。证明了LSTM深度神经网络具有较高的预测精度，可以有效地预测股市时间序列。