Generalizing Dynamic Patching Gains

Background: Time series foundation models require efficient tokenization strategies to balance the preservation of fine-grained temporal fidelity with computational efficiency for long-context training.

Question / Future Work: The paper introduced a hybrid tokenizer combining an SSM encoder with dynamic, relative deviation-based patching. Future work could explore generalizing the observed efficiency and performance gains to other forecasting backbones beyond the specific Time-MoE structure used in the main experiments. Preliminary results suggest transferability, but scaling up the pretraining of these alternative backbones with the dynamic tokenizer is a direction for future validation.