LSTPrompt：通过长短期提示将大语言模型用作零样本时间序列预测器
LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting
AI总结：
本文提出LSTPrompt方法，将时间序列预测分解为短期和长期子任务并定制提示，引导大语言模型定期重新评估预测机制，在零样本TSF任务中取得优于现有提示方法且可比肩基础TSF模型的性能。
摘要：
时间序列预测（TSF）在现实场景中有广泛应用。对现成的大语言模型（LLMs）进行提示，在保持计算效率的同时展现出强大的零样本TSF能力。然而，现有提示方法将TSF过度简化为语言的下一个标记预测，忽视了其动态特性，并且缺乏与链式思维（Chain-of-Thought）等最先进提示策略的整合。因此，我们提出LSTPrompt，这是一种在零样本TSF任务中提示LLMs的新方法。LSTPrompt将TSF分解为短期和长期预测子任务，并为每个子任务定制提示。LSTPrompt引导LLMs定期重新评估预测机制以增强适应性。广泛评估表明，LSTPrompt的性能始终优于现有提示方法，并且与基础TSF模型相比具有竞争力的结果。
英文摘要：
Time-series forecasting (TSF) finds broad applications in real-world scenarios. Prompting off-the-shelf Large Language Models (LLMs) demonstrates strong zero-shot TSF capabilities while preserving computational efficiency. However, existing prompting methods oversimplify TSF as language next-token predictions, overlooking its dynamic nature and lack of integration with state-of-the-art prompt strategies such as Chain-of-Thought. Thus, we propose LSTPrompt, a novel approach for prompting LLMs in zero-shot TSF tasks. LSTPrompt decomposes TSF into short-term and long-term forecasting sub-tasks, tailoring prompts to each. LSTPrompt guides LLMs to regularly reassess forecasting mechanisms to enhance adaptability. Extensive evaluations demonstrate consistently better performance of LSTPrompt than existing prompting methods, and competitive results compared to foundation TSF models.
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。
