TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents

• URL: http://arxiv.org/abs/2502.11418v1
• Date: Mon, 17 Feb 2025 04:17:27 GMT
• Title: TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents
• Authors: Geon Lee, Wenchao Yu, Kijung Shin, Wei Cheng, Haifeng Chen,
• Abstract summary: TimeCAP is a time-series processing framework that creatively employs Large Language Models (LLMs) as contextualizers of time series data. 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. Experimental results on real-world datasets demonstrate that TimeCAP outperforms state-of-the-art methods for time series event prediction.
• Score: 52.13094810313054
• License:
• Abstract: 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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