Rethinking the Role of LLMs in Time Series Forecasting

This research paper evaluates the efficacy of **Large Language Models (LLMs)** in the field of **time series forecasting (TSF)** through a massive empirical study. While previous scholars argued that LLMs offer minimal benefits over standard models, this study utilizes **8 billion observations** to prove that LLMs significantly enhance **cross-domain generalization** and predictive accuracy. The authors identify that **pre-alignment strategies**, which map numerical data to word embeddings, generally outperform post-alignment fine-tuning. Their analysis reveals that LLMs are particularly powerful when dealing with **distribution shifts** and **complex temporal dynamics** rather than simple seasonal patterns. Furthermore, the paper introduces a **routing mechanism** to show that models adaptively choose when to utilize LLM logic based on data complexity. Ultimately, the findings provide a framework for using **pretrained world knowledge** to improve forecasting across diverse real-world scenarios.