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 work, we present Time-LLM, a reprogramming framework to repurpose LLMs for general time series forecasting with the backbone language models kept intact. We begin by reprogramming 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, we propose Prompt-as-Prefix (PaP), which enriches the input context and directs the transformation of reprogrammed input patches. The transformed time series patches from the LLM are finally projected to obtain the forecasts. Our comprehensive evaluations demonstrate that Time-LLM is a powerful time series learner that outperforms state-of-the-art, specialized forecasting models. Moreover, Time-LLM excels in both few-shot and zero-shot learning scenarios.

翻译：时间序列预测在众多实际动态系统中具有重要意义，并已得到广泛研究。与自然语言处理和计算机视觉不同——这两个领域的一个大型模型可应对多项任务——时间序列预测模型通常是专用的，需要针对不同任务和应用设计不同的架构。尽管预训练基础模型在自然语言处理和计算机视觉领域取得了显著进展，但其在时间序列领域的发展受到数据稀疏性的制约。近期研究表明，大型语言模型具备对复杂标记序列的强健模式识别与推理能力。然而，如何有效对齐时间序列数据与自然语言的模态以利用这些能力仍是一个挑战。在本工作中，我们提出Time-LLM——一种重编程框架，在保持骨干语言模型不变的前提下将大型语言模型用于通用时间序列预测。我们首先通过文本原型对输入时间序列进行重编程，随后将其输入冻结的大型语言模型以对齐两种模态。为增强大型语言模型对时间序列数据的推理能力，我们提出前缀提示方法（Prompt-as-Prefix, PaP），该方法丰富输入上下文并引导重编程输入块的转换。最终，从大型语言模型输出的转换后时间序列块被投影以获得预测结果。全面的评估表明，Time-LLM是一个强大的时间序列学习器，其性能优于最先进的专用预测模型。此外，Time-LLM在少样本和零样本学习场景中均表现出色。