Hybrid Deep Learning Approach with Bi-LSTM and Bi-GRU for Effective Stock Price Prediction

It is difficult to predict stock prices since financial markets are highly non-linear. Because of their ability to spot complex patterns and connections in price. By the advancements of deep learning we can predict the future price of stock more efficiently than the machine learning algorithms. By using the advanced recurrent neural network structures, like the Bidirectional Long Short-Term Memory (BiLSTM) and the Bidirectional Gated Recurrent Unit (BiGRU), for predicting stock price can be seen in this paper. These models can identify patterns more linear by learning from past and future dependencies in constant data through bidirectional model. In the hybrid approach, BiLSTM and BiGRU models are trained using historical stock price data and extract the meaning patterns from these. Here we use some metrics for evaluating the hybrid model like RMSE, MAE,R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>and MDA. The metrics show the RMSE score is at 0.91 and MAE (Mean Absolute Error) at 0.89. Comparing to individual models this hybrid shows a clear edge with much lower values. It explains 92% of stock price variance, outperforming RNN, LSTM, and GRU.The result suggest that these models can be useful tools for financial forecasting, giving stock market analysts and investors decision making.