Study on the application of LSTM-LightGBM Model in stock rise and fall prediction

Abstract

This paper proposes a hybrid financial time series forecast model based on LSTM and LightGBM, namely LSTM_LightGBM model. Use the LightGBM model to train the processed stock historical data set, and save the training results. Then the opening price, closing price, highest price, lowest price, trading volume and adjusted closing price are separately input into the LSTM model for prediction. The prediction result of each attribute is used as the test set of the prediction after LightGBM training. Constantly adjust the parameters of each model, and finally get the optimal stock price forecast model. The model is validated with the rise and fall of AAPL stock. Through the comparison of evaluation index root mean square error RMSE, mean absolute error MAE, prediction accuracy Accuracy and f1_score. It is found that the LSTM_LightGBM model exhibits stable and better prediction performance in the stock prediction. That is to say, the LSTM_LightGBM model proposed in this paper is stable and feasible in the stock price fluctuation forecast.