Time series forecasting holds significant importance in many real-world dynamic systems and has been extensively studied. Unlike natural language process (NLP) and computer vision (CV), where a single large model can tackle multiple tasks, models for time series forecasting are often specialized, necessitating distinct designs for different tasks and applications. While pre-trained foundation models have made impressive strides in NLP and CV, their development in time series domains has been constrained by data sparsity. Recent studies have revealed that large language models (LLMs) possess robust pattern recognition and reasoning abilities over complex sequences of tokens. However, the challenge remains in effectively aligning the modalities of time series data and natural language to leverage these capabilities. In this chapter, we present Time-LLM (Jin et al., Time-LLM: Time series forecasting by reprogramming large language models. In: The Twelfth International Conference on Learning Representations (2024)), a reprogramming framework to repurpose pretrained LLMs for general time series forecasting with the backbone language models kept intact. Time-LLM reprograms the input time series with text prototypes before feeding it into the frozen LLM to align the two modalities. To augment the LLM’s ability to reason with time series data, Prompt-as-Prefix (PaP) is proposed to enrich the input context and direct the transformation of reprogrammed input patches. The transformed time series patches from the LLM are finally projected to obtain the forecasts. The comprehensive evaluations demonstrate that Time-LLM is a powerful time series learner that outperforms state-of-the-art, specialized forecasting models.