How do you prevent overfitting in deep time series models?

Overfitting is prevented using regularization, dropout, early stopping, and proper validation strategies like time-based splits.

Deep time series models can easily overfit due to high parameter counts and temporal dependencies. Techniques include dropout in recurrent/attention layers, L2 regularization, early stopping based on validation loss, and data augmentation. Time-aware validation (walk-forward validation) is critical to avoid leakage and ensure realistic evaluation.