StockBiLSTM: Utilizing an Efficient Deep Learning Approach for Forecasting Stock Market Time Series Data

The article introduces a novel approach for forecasting stock market prices, employing a computationally efficient Bidirectional Long Short-Term Memory (BiLSTM) model enhanced with a global pooling mechanism. Based on the deep learning framework, this method leverages the temporal dynamics of stock data in both forward and reverse time frames, enabling enhanced predictive accuracy. Utilizing datasets from significant market players-HPQ, Bank of New York Mellon, and Pfizer-the authors demonstrate that the proposed single-layered BiLSTM model, optimized with RMSprop, significantly outperforms traditional Vanilla and Stacked LSTM models. The results are quantitatively evaluated using root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination (R<^>2), where the BiLSTM model shows a consistent improvement in all metrics across different stock datasets. We optimized the hyperparameters tuning using two distinct optimizers (ADAM, RMSprop) on the HPQ, New York Bank, and Pfizer datasets. The dataset has been preprocessed to account for missing values, standardize the features, and separate it into training and testing sets. Moreover, line graphs and candlestick charts illustrate the models' ability to capture stock market trends. The proposed algorithms attained respective RMSE values of 0.413, 0.704, and 0.478. the proposed algorithms attained respective RMSE values of 0.413, 0.704, and 0.478. The results show the proposed methods' superiority over recently published models. In addition, it is concluded that the proposed single-layered BiLSTM-based architecture is computationally efficient and can be recommended for real-time applications involving Stock market time series data.