| Summary: | 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.
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