← Back to Projects Retail / Supply Chain 
Time Series Demand Forecasting
Multi-model forecasting combining Prophet, LSTM, and XGBoost for retail demand prediction with seasonality detection, anomaly handling, and ensemble forecasts.
Overview
Multi-model demand forecasting system for retail supply chain optimization.
Architecture
- Prophet for trend and seasonality decomposition
- LSTM (PyTorch) for sequential pattern learning
- XGBoost for feature-rich regression
- Ensemble combiner with dynamic weighting
- Rolling-window backtesting framework
Key Features
- Automatic seasonality detection
- Anomaly-aware training pipeline
- Interactive forecast visualization
- Model comparison dashboard