GRU Crypto Price Forecast Model Training
GRU (Gated Recurrent Unit) is a simplified version of LSTM. Instead of three gates (input, forget, output) like LSTM, GRU has two: reset gate and update gate. This makes GRU faster to train and inference while maintaining comparable quality on most tasks.
GRU vs LSTM: when to choose:
- GRU preferable when: data < 1 year, need fast inference, limited resources, quick prototyping
- LSTM preferable when: lots of data (3+ years), need long-term memory (200+ candles), requires fine memory control
Architecture features:
- Temporal attention for better representation
- Bidirectional GRU for richer features
- Monte Carlo Dropout for uncertainty estimation
- Multi-step forecasting with separate heads
Computational requirements:
- Training on CPU: ~2 hours for 2 years of 1h data
- Training on GPU (T4): ~15 minutes
- Inference: < 5ms on CPU for single batch
Ensemble approach: multiple GRU models trained with different seeds and hyperparameters are more stable than single model.
Develop and train GRU ensemble with temporal awareness, Monte Carlo Dropout for uncertainty, multi-step forecasting and production-ready API.