Predicting the stock prices of listed companies based on the LSTM model of recurrent neural network
DOI:
https://doi.org/10.61173/av49xn45Keywords:
LSTM, RNN, KFC dataset, error analysis, MAE, RMSE, MAPEAbstract
This experiment uses the LSTM model and establishes a two-layer LSTM structure to enhance the learning ability of long-term dependencies. The Dropout layer can prevent the model from overfitting historical data during training. Normalization, time series sliding window technology, and 80-20 training-test split method are used for data preprocessing. MAE, RMSE, and MAPE are used for evaluation to compare the predicted value with the actual value.
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