AI-Powered Stock Market Price Prediction System
A stock market forecasting system that predicts future price trends using ARIMA, LSTM, and historical market data analysis.
Stock market data changes continuously, and understanding future price movement requires analyzing historical trends, price patterns, and trading behavior. Manual analysis can be slow and may miss important signals hidden inside large financial datasets.
This project solves that problem by building an AI-powered stock market price prediction system that forecasts stock prices for the next 60 days. It uses both traditional statistical forecasting with ARIMA and deep learning prediction with LSTM to capture market trends from historical data.
The system processes important stock metrics such as Open, Close, High, Low, and Volume. It applies preprocessing techniques like normalization and rolling statistics, then trains forecasting models and evaluates their accuracy to improve prediction stability and reliability.
Key benefits
- Predicts stock prices for a 60-day future window
- Uses both ARIMA and LSTM forecasting models
- Processes Open: Close, High, Low, and Volume data
- Applies normalization and rolling statistics for better model input
- Provides visual insights for market trend analysis
What's included
- Stock data preprocessing workflow
- ARIMA-based forecasting model
- LSTM deep learning prediction model
- Feature engineering from market price metrics
- Visualization and accuracy evaluation system
Use Cases
- Stock price trend forecasting
- Financial market analysis
- Investment research support
- Time-series prediction study
- Trading dashboard integration
Key deliverables
- AI-powered stock forecasting system
- ARIMA and LSTM prediction models
- Data preprocessing and feature engineering pipeline
- Forecast visualization dashboard
- Model evaluation and accuracy analysis workflow