LLM Pretraining Shapes a Generalizable Manifold: Insights into Cross-Modal Transfer to Time Series
A new research paper explores how large language models (LLMs) pretrained on text can be effectively used for time-series forecasting. The study demonstrates that language pretraining equips transformers with a reusable manifold, enabling them to learn time-series dynamics without direct supervision. This pretraining not only improves the optimization process but also allows for low-dimensional alignment during fine-tuning, effectively projecting numerical dynamics onto task-relevant directions. AI
IMPACT Demonstrates LLMs can be adapted for time-series forecasting by leveraging pre-trained structures, potentially improving efficiency and accuracy in numerical dynamics prediction.