Long Short-Term vs Gated Recurrent Unit Recurrent Neural Network For Google Stock Price Prediction

Deep Learning has proven its powerful performance in many fields as it is the sub-component of Artificial Intelligence. The use of traditional statistics methods in forecasting time series are less practicality and gives less valuable prediction. The aim of this study is to propose Recurrent Neural Network (RNN) model that suitable for forecasting Google Stock Price time series data. In this study, RNN with Long Short-Term (LSTM) and Gated Recurrent Unit (GRU) architectures are proposed as predictive models known as RNN-LSTM (2), RNN-LSTM (3), RNN-GRU (2), and RNN-GRU (3). The experimental results revealed that RNN-GRU (3) was the best model with lowest error measurements of Root Mean Square Error (RMSE), Median Absolute Percentage Error (MdAPE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Directional Accuracy (MDA). The proposed model showed its capability and applicability in predicting the future values of Google stock price data with good accuracy and it can be used to predict multi-step ahead values. Evident from this analysis, it is proven that the proposed RNN-GRU (3) provides a promising alternative technique in forecasting time-series data.