Project Overview
A sophisticated forecasting system that predicts crude oil prices based on historical data and market indicators. The project combines time series analysis with machine learning approaches to provide accurate price predictions, valuable for financial analysis and commodity trading decisions.
Key Features
- Historical price data analysis and preprocessing
- Multiple forecasting models (ARIMA, LSTM, Linear Regression)
- Feature engineering for market indicators
- Model performance comparison and validation
- Visualization of price trends and predictions
- Confidence intervals for prediction accuracy
Technical Challenges
- Handling volatile and non-stationary price data
- Incorporating external market factors and events
- Balancing model complexity with prediction accuracy
- Managing overfitting in time series models