Training Tips and Variants
- Preprocess time series (normalize features, windowing); handle variable lengths (padding/truncation).
- Experiment with network depth, hidden units, and regularization (dropout) to avoid overfitting.
- Consider bidirectional LSTM or stacking multiple LSTM layers for complex patterns.
- GRU is a simpler gated RNN that combines forget/input gates into one update gate.
- Monitor gradients during training; use gradient clipping to stabilize learning if needed.