在大型语言模型（LLM）的时间序列预测任务中，上下文信息可能比推理更重要
Context information can be more important than reasoning for time series forecasting with a large language model
AI总结：
本文探索LLMs在时间序列预测中的特性，发现无普适提示方法，仅提供适当上下文信息即可达到与最佳推理提示相当的性能，还指出了LLMs在提示遵循、代数计算及语义理解上的三个弱点。
摘要：
随着大型语言模型（LLMs）的发展，利用LLMs处理时间序列任务的兴趣日益浓厚。本文通过考虑各种现有及提出的提示技术，探索了LLMs在时间序列预测中的特性，评估了其对短时间序列和长时间序列的预测表现。研究发现，没有单一提示方法具有普适性；仅提供与时间序列相关的适当上下文信息（无需额外推理提示），即可达到与各场景下最佳提示方法相当的性能。基于此观察，在时间序列预测中，提供适当上下文信息可能比特定推理提示更关键。此外，研究还指出了时间序列预测提示技术的几个弱点：一是LLMs常无法遵循提示描述的流程；二是当推理步骤涉及含多个操作数的简单代数计算时，LLMs常计算不准确；三是LLMs有时会误解提示语义，导致响应不完整。
英文摘要：
With the evolution of large language models (LLMs), there is growing interest in leveraging LLMs for time series tasks. In this paper, we explore the characteristics of LLMs for time series forecasting by considering various existing and proposed prompting techniques. Forecasting for both short and long time series was evaluated. Our findings indicate that no single prompting method is universally applicable. It was also observed that simply providing proper context information related to the time series, without additional reasoning prompts, can achieve performance comparable to the best-performing prompt for each case. From this observation, it is expected that providing proper context information can be more crucial than a prompt for specific reasoning in time series forecasting. Several weaknesses in prompting for time series forecasting were also identified. First, LLMs often fail to follow the procedures described by the prompt. Second, when reasoning steps involve simple algebraic calculations with several operands, LLMs often fail to calculate accurately. Third, LLMs sometimes misunderstand the semantics of prompts, resulting in incomplete responses.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
