Time-Prompt: 集成异构提示以解锁时间序列预测中的LLM
Time-Prompt: Integrated Heterogeneous Prompts for Unlocking LLMs in Time Series Forecasting
AI总结：
本文提出Time-Prompt框架，通过构建统一的提示范式、设计语义空间嵌入和跨模态对齐模块以及高效微调LLM参数，提升时间序列预测性能，并在碳排放数据集上验证其有效性。
摘要：
时间序列预测旨在建模变量间的时序依赖关系以推断未来状态，对现实世界场景具有重要性和广泛应用。尽管基于深度学习的方法已取得显著进展，但其在长期预测中仍表现不佳。最近研究表明，大型语言模型（LLMs）在时间序列预测中表现出色，但其在该任务中的实用性仍存疑。为此，我们提出Time-Prompt框架，旨在激活LLMs进行时间序列预测。具体而言，我们首先构建了一个统一的提示范式，利用可学习的软提示引导LLM的行为，并利用文本化的硬提示增强时间序列表示。其次，为了增强LLM对预测任务的全面理解，我们设计了一个语义空间嵌入和跨模态对齐模块，以实现时序和文本数据的融合。最后，我们利用时间序列数据高效地微调LLM的参数。此外，我们专注于碳排放领域，旨在为全球碳中和做出贡献。在6个公开数据集和3个碳排放数据集上的综合评估表明，Time-Prompt是一个强大的时间序列预测框架。
英文摘要：
Time series forecasting aims to model temporal dependencies among variables for future state inference, holding significant importance and widespread applications in real-world scenarios. Although deep learning-based methods have achieved remarkable progress, they still exhibit suboptimal performance in long-term forecasting. Recent research demonstrates that large language models (LLMs) achieve promising performance in time series forecasting, but this progress is still met with skepticism about whether LLMs are truly useful for this task. To address this, we propose Time-Prompt, a framework for activating LLMs for time series forecasting. Specifically, we first construct a unified prompt paradigm with learnable soft prompts to guide the LLM's behavior and textualized hard prompts to enhance the time series representations. Second, to enhance LLM' comprehensive understanding of the forecasting task, we design a semantic space embedding and cross-modal alignment module to achieve fusion of temporal and textual data. Finally, we efficiently fine-tune the LLM's parameters using time series data. Furthermore, we focus on carbon emissions, aiming to provide a modest contribution to global carbon neutrality. Comprehensive evaluations on 6 public datasets and 3 carbon emission datasets demonstrate that Time-Prompt is a powerful framework for time series forecasting.
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。
