Challenge and Opportunity: Deep Learning-Based Stock Price Prediction by Using Bi-Directional LSTM Model

Stock price prediction is a challenging and important task in finance, with many potential applications investment, risk management, portfolio optimization. In this paper, we propose bi-directional long short-term memory (Bi-LSTM) model for predicting the future of stock based on its historical prices. The Bi-LSTM variant popular LSTM that capable processing input sequences both forward backward directions, allowing it to capture short- long-term dependencies data. We apply data Apple Inc. evaluate performance using mean squared error (MSE) visual inspection actual vs. predicted Our experiments show able make accurate predictions testing some trends patterns data, although may struggle sudden changes market. Overall, our results suggest promising tool has finance investment.