DemandCast
Large-scale demand forecasting pipeline — LSTM with Temporal Attention achieving 22% lower MAPE across 50K+ products
End-to-end demand forecasting system for predicting daily SKU-level demand across 50K+ products, comparing classical and deep learning approaches.
What I built:
- Benchmarked ARIMA, Prophet, XGBoost, and LSTM — LSTM with Temporal Attention won with 22% lower MAPE vs ARIMA baseline
- Real-time inference API with FastAPI + Redis caching for sub-100ms predictions
- Kafka-based streaming pipeline to ingest live sales data
- Model monitoring layer that detects data drift and triggers automated retraining
Tech stack: Python · PyTorch · XGBoost · FastAPI · Kafka · Redis · PostgreSQL
| Metric | Value |
|---|---|
| MAPE improvement | 22% |
| Products modeled | 50K+ |
| Status | Ongoing |