Deep models have demonstrated remarkable performance in time series forecasting.

Hyperparameter Optimization (HPO) plays a pivotal role in unleashing the potential of iterative machine learning models.

This deliberation, however, comes at the cost of significantly increased inference complexity.

The integration of the PDE into a loss function endows PINNs with a distinctive feature to require computing derivatives of model up to the PDE order.