Research on gated recurrent unit based stock price prediction model with multi-features under low time scale

Under the development of people's living environment, more and more people are willing to use their money to invest in financial projects such as stocks and insurance. Nowadays, science and technology are widely applied in people's life. Machine learning is one of them. Machine learning is particularly important to apply to stock forecasting to better meet the requirements of people who want to gain more benefits. The purpose of this work is to compare using GRU, LSTM, and bidirectional LSTM's MAE and RMSE on the closing price. The method of this experiment is to compare with root mean squared error (RMSE) and mean absolute error (MSE) after the input variables of the past 63 trading days passing through those three models. The results of the experiment indicate that MAE of GRU model is lowest. Still, only nine of fifteen experiments show that RMSE of GRU model is lowest, and five of fifteen experiments show that RMSE of LSTM is lowest. One of fifteen experiments expresses that RMSE of bidirectional LSTM has the lowest RMSE. Thus, GRU is considered to be the best model for stock price regression.