Stock Price Prediction using LSTM
A deep learning-based stock price prediction system built using Long Short-Term Memory (LSTM) networks to model temporal dependencies in historical financial data. The model learns sequential patterns and forecasts future price movements based on past trends.
Technical Highlights
- Time-series preprocessing & normalization
- Sliding window sequence generation
- LSTM-based sequential model architecture
- Train-test split for forward validation
- Loss optimization using MSE
- Prediction vs Actual price visualization
Model Architecture
The architecture consists of stacked LSTM layers followed by dense output layers to capture long-term dependencies and nonlinear market patterns. The model is trained on historical closing prices to predict future trends.