Time Series Stock Price Forecasting: An LSTM Neural Network Approach for Market Prediction

This study creates a more advanced Long Short-Term Memory (LSTM) neural network structure for predicting stock prices. It does this by using a new three-layer framework (200-200-64 units) and improving the dropout regularization. The methodology incorporates dynamic window normalization and automated feature engineering to address the challenges of financial time series data. Empirical validation using Apple (AAPL) stock demonstrates the model's effectiveness in capturing both short-term fluctuations and long-term market trends. The system implements sophisticated temporal sequence processing and risk quantification techniques, showing significant improvements over traditional forecasting methods. The research contributes to both theoretical understanding through its innovative architecture design and practical applications in algorithmic trading systems. The results show that the predictions were more accurate than expected and gave us new information about how markets behave, which built a strong foundation for financial forecasting.