TimeLLM: Time Series Forecasting by Reprogramming Large Language Models

Accurate forecasting of time series is essential to many dynamic real-world systems and has attracted extensive research attention. Unlike natural language processing or computer vision, where one large model can often address multiple tasks, most existing forecasting solutions are highly specialized and confined to the single time series data modality. Advancements in multimodal time series foundation models have significantly lagged behind other domains, mainly because large, high-quality time series corpora remain scarce. At the same time, recent evidence suggests that large language models (LLMs) excel at understanding and reasoning across long token sequences. Exploiting those capabilities for forecasting…