Time-LLM is an official implementation of a research paper that adapts pre-trained LLMs for time series forecasting without fine-tuning. The method reprograms input time series by remapping them into natural language descriptions compatible with LLM inputs, leveraging the knowledge embedded in foundation models. It evaluates across multiple benchmark time series datasets, demonstrating competitive forecasting performance by aligning time series with language model capabilities.