A Neural Network-Based Stock Prediction System

This program implements a neural network-based approach for stock market prediction. By analyzing historical data and fitting pattern curves using machine learning algorithms (such as backpropagation and gradient descent optimization), the system accurately forecasts future stock price movements. The implementation includes data preprocessing modules for normalization, LSTM or GRU layers for time series analysis, and output layers for price trend predictions. With strong predictive capabilities demonstrated through metrics like RMSE and MAPE, this tool assists investors in making informed investment decisions. Furthermore, the program incorporates adaptive adjustment mechanisms that continuously learn from changing market conditions, ensuring prediction accuracy and reliability through techniques like rolling window validation and hyperparameter tuning. Overall, this represents a powerful and practical tool that leverages deep learning architectures to help investors achieve better investment returns through data-driven insights.