Variable-Rate Patching Scaling

Background: The scalability of sequence models like Transformers is often limited by the quadratic complexity concerning sequence length, leading to high computational costs during pretraining on long contexts.

Question / Future Work: The method demonstrated efficient large-scale pretraining using dynamic context compression. Exploring this variable-rate patching and compression strategy further in the context of time series forecasting foundation models, especially for even larger scales or different data characteristics, remains a promising research direction.