用大模型理解时间序列上下文，预测准确率提升28.75%。
TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents
- 用两个大模型代理分别生成上下文摘要和预测，实现分步增强。
- 在真实数据集上，F1得分平均提升28.75%，优于现有方法。
- 适合需要高精度事件预测的医疗、金融等场景。
时间序列数据在气候建模、医疗监测和金融分析等领域至关重要。理解真实世界时间序列数据的上下文信息对于准确可靠的事件预测至关重要。本文提出TimeCAP，一个创造性地将大型语言模型（LLMs）作为时间序列上下文解析器的时间序列处理框架，扩展了其传统预测用途。TimeCAP包含两个独立的LLM代理：一个生成捕捉时间序列上下文的文本摘要，另一个利用此丰富摘要进行更明智的预测。此外，TimeCAP采用多模态编码器，与LLM代理协同工作，通过上下文示例互增输入，提升预测性能。在真实数据集上的实验结果表明，TimeCAP在时间序列事件预测方面优于现有最优方法，包括使用LLM作为预测器的方法，在F1分数上平均提升28.75%。
原文摘要 · Abstract (English)
Time series data is essential in various applications, including climate modeling, healthcare monitoring, and financial analytics. Understanding the contextual information associated with real-world time series data is often essential for accurate and reliable event predictions. In this paper, we introduce TimeCAP, a time-series processing framework that creatively employs Large Language Models (LLMs) as contextualizers of time series data, extending their typical usage as predictors. TimeCAP incorporates two independent LLM agents: one generates a textual summary capturing the context of the time series, while the other uses this enriched summary to make more informed predictions. In addition, TimeCAP employs a multi-modal encoder that synergizes with the LLM agents, enhancing predictive performance through mutual augmentation of inputs with in-context examples. Experimental results on real-world datasets demonstrate that TimeCAP outperforms state-of-the-art methods for time series event prediction, including those utilizing LLMs as predictors, achieving an average improvement of 28.75% in F1 score.
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