As a recognized complex dynamic system, the stock market has many influencing factors, such as nonstationarity, nonlinearity, high noise, and long memory. It is difficult to explain it simply through mathematical models. Therefore, the analysis and prediction of the stock market have been a very challenging job since long time. Therefore, this paper adopts an encoder-decoder model of attention mechanism, adding attention mechanism from two aspects of feature and time. Both encoder and decoder use LSTM neural network. This method solves two problems in time series prediction; the first problem is that multiple input features have different degrees of influence on the target sequence, the feature attention mechanism is used to deal with this problem, and the weights of different input features can be obtained. A more robust feature association relationship is obtained; the second problem is that the data before and after the sequence have a strong time correlation. The time attention mechanism is used to deal with this problem, and the weights at different time points can be obtained to obtain more robustness and good timing dependencies. The simulation and experimental results show that the introduction of the attention mechanism can obtain lower forecast errors, which proves the effectiveness of the model in dealing with stock forecasting problems.

中文翻译：

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一种基于深度神经网络的股票趋势预测算法

股票市场作为公认的复杂动态系统，具有非平稳性、非线性、高噪声、长记忆等诸多影响因素。仅仅通过数学模型很难解释。因此，长期以来，股票市场的分析和预测一直是一项非常具有挑战性的工作。因此，本文采用注意力机制的encoder-decoder模型，从特征和时间两个方面加入注意力机制。编码器和解码器都使用 LSTM 神经网络。该方法解决了时间序列预测中的两个问题；第一个问题是多个输入特征对目标序列的影响程度不同，使用特征注意力机制来处理这个问题，可以得到不同输入特征的权重。得到了更健壮的特征关联关系；第二个问题是序列前后的数据有很强的时间相关性。使用时间注意力机制来处理这个问题，可以得到不同时间点的权重，以获得更强的鲁棒性和良好的时序依赖性。仿真和实验结果表明，引入注意力机制可以获得较低的预测误差，证明了该模型在处理股票预测问题方面的有效性。