Abstract: The stock market is a nonlinear dynamics system, which is changeable and complicated, and the price of stock is a kind of data with the character of time sequence. Given that, this thesis selected the model of Gated Recurrent Unit（GRU） with the function of time memory to deal with the problem of predicting time series data. The thesis selected daily closing price data of 18 securities industry stock in Shanghai, and the deadline of the data is December 29th, 2017. The data volume of the thesis is 1000 days per stock. To predict the closing price of the stock in the next 10 days, the thesis made empirical research. The testing and validation errors of GRU recurrent neural network is smaller than that of the other two models in the same type of error, while the accuracy of the prediction of closing price in next 10 day of Gated Recurrent Unit（GRU） has reached 98.3%, showed in the empirical results. This has embodied the strong learning ability and generalizing ability of Gated Recurrent Unit（GRU）. On the other hand, comparing the test error, variance in forecast closing price, and validation error of testing of Gated Recurrent Unit（GRU）on the sequence length of 240 days, 120 days, 60 days, and it suggests that the predicting accuracy are all in a high precision. However, the testing result on the sequence length of 240 days has a lower variance obviously, showing its better stability.

Key words: stock market, time series, GRU, NN, closing price