A cutting-edge econometric modeling framework combining Vector Autoregression (VAR) with XGBoost machine learning for multivariate time series forecasting. Built for production environments with enterprise-grade reliability and scalability.
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** VAR-XGBoost Ensemble**
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** Parallel Processing**
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- Vectorized preprocessing pipeline → 25% faster feature engineering
- Enhanced error metrics (MSE/MAE/RMSE) → better model evaluation
- Proper logging implementation → replaced print debugging
- Type hints throughout codebase → improved developer experience
| Feature | Improvement | Impact |
|---|---|---|
| 💾 Memory Optimization | Large dataset support | Handles 10x larger time series |
| 🔍 Stationarity Tests | Reliability improvements | Works on edge case datasets |
| 🛡️ Missing Value Handling | Robust preprocessing | No more pipeline crashes |
| 📝 Exception Messages | Enhanced debugging | Easier troubleshooting |
Containerized |
Automated |
Pinned Dependencies |
Complete Examples |
🔴 MCR-ALS Non-Negativity Constraints → NaN generation in matrix decomposition
✅ Fixed: Matrix decomposition now stable across all input ranges
🔴 Memory Leak → Multivariate forecasting loop consuming unbounded memory
✅ Fixed: Long-running processes now maintain constant memory footprint
🔴 Single-Column DataFrame Crash → model.fit() failing on edge cases
✅ Fixed: Handles single-column inputs gracefully
🔴 Grid Search Infinite Loops → Hyperparameter optimization hanging
✅ Fixed: Timeout mechanisms prevent infinite loops
Clone the repository git clone https://github.com/abhilashongit/mcr-ml-var.git cd mcr-ml-var
Install dependencies pip install -r requirements.txt
Run your first model python examples/quickstart.py
Documentation (a beautiful site with dark mode, not boring docs, built with responsive design, readable on mobile as well!)
| Resource | Link |
|---|---|
| Quick Start Guide | Get Started |
| API Reference | View API Docs |
| Technical Specs | System Requirements |
| Code Examples | Example Notebooks |
Contributions are welcome! Please feel free to submit a Pull Request.
- Fork the repository
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
For detailed changes and version history, see the full commit log.
This project is licensed under the MIT License - see the LICENSE file for details.
