Transformer Crypto Price Forecast Model Training
Transformer architecture developed for NLP shows excellent results in time series tasks. Self-attention mechanism allows model to directly access any historical moment without recurrent passage, eliminating gradient vanishing problems on long sequences.
Key advantages over LSTM:
- Direct attention to long-range dependencies
- Full parallelization during training
- Better on large datasets
- Attention weights provide interpretability
TFT (Temporal Fusion Transformer): specialized for time series with quantile loss for probabilistic forecasting.
PatchTST: applies patching like Vision Transformer for computational efficiency and better local pattern capture.
Develop and train Transformer models with walk-forward validation, multi-asset training and production deployment via FastAPI.