In today’s fast-moving financial world, stock markets are extremely dynamic and packed with vast amounts of data. Prices fluctuate constantly based on global events, investor mood, economic indicators, and countless other factors. Predicting these movements is challenging because stock market data is nonlinear, highly volatile, and often unpredictable. Traditional forecasting approaches—such as ARIMA and linear regression—struggle with these complexities since they assume stable patterns and cannot fully capture deep temporal relationships. As data-driven decision-making continues to shape financial systems, there is a strong need for intelligent predictive models capable of learning and adapting to changing market behavior.

In this study, we propose an LSTM-based deep learning model for real-time stock price prediction. Long Short-Term Memory (LSTM) networks address the limitations of conventional RNNs by overcoming the vanishing gradient issue through the use of memory gates. These mechanisms allow the model to retain and manage long-term dependencies in sequential data. Historical stock data obtained from Yahoo Finance undergo several preprocessing steps—such as normalization, smoothing, and time windowing—to improve temporal structure and ensure higher predictive accuracy. These processed sequences are then fed into the LSTM architecture for training, enabling the model to learn important sequential patterns and nonlinear relationships between past and future price fluctuations.

The final model uses multiple stacked LSTM layers followed by dense output layers, which proved to be a strong configuration for learning complex time-series patterns. Mean Squared Error (MSE) is used as the loss function, while the Adam optimizer regulates weight updates for efficient training. The design aims to strike a balance between model depth and training speed to support both short-term and long-term forecasting.

To evaluate performance, metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R²) were used. Experimental tests on Apple (AAPL) and Tesla (TSLA) stock data showed that the multi-layer LSTM model captures temporal dependencies more effectively than traditional methods like ARIMA and simple RNNs. The proposed system consistently produced lower RMSE values and higher R² scores, indicating superior prediction accuracy.

Overall, the LSTM-based architecture provides an improved way to model stock price trends by uncovering hidden temporal patterns and offering more trustworthy predictions. This work creates a foundation for next-generation hybrid models that can combine sentiment analysis, technical indicators, and attention mechanisms to push forecasting accuracy even further.