Long Short-Term Memory and Gated Recurrent Unit Modeling for Stock Price Forecasting

Abstract

The rapid advancement of deep learning technology offers significant benefits, particularly for time series data forecasting. LSTM and GRU are two deep learning methods used for forecasting. This study aims to compare the LSTM and GRU methods in predicting the stock prices of PT Mayora Indah Tbk, listed on the Indonesia Stock Exchange (IDX), accessed through Yahoo Finance. The model architecture was developed using combinations of data splitting, learning rate, epoch count, and neuron count. The combinations used in this study include data splits of 70:30 and 80:20, with varying parameter combinations of learning rates at 0.01, 0.001, and 0.0001, epoch counts of 50, 100, and 150, and neuron counts of 50 and 100. The results indicate that the GRU method outperforms the LSTM method in prediction accuracy. The RMSE and MAPE tests show that the GRU method yields RMSE and MAPE of 34.4233 and 1.27%, respectively, compared to the LSTM method with RMSE and MAPE of 35.3775 and 1.28%. The best GRU architecture was achieved with a learning rate of 0.001, 50 neurons, and 150 epochs, while the best LSTM architecture was achieved with a learning rate of 0.001, 100 neurons, and 150 epochs. The optimal architecture for both methods was found with a data split of 70:30