用大模型提升时间序列异常点检测准确率和解释力
LLM-Augmented Changepoint Detection: A Framework for Ensemble Detection and Automated Explanation
- 集成十种检测算法，自动选最优结果
- 大模型自动生成变化事件的背景说明
- 支持私有数据，可结合用户文档生成解释
本文提出一种新型时间序列突变点检测框架，融合十种不同统计检测算法的集成方法与大语言模型（LLM），在提升检测精度与鲁棒性的同时，增强对制度变迁的可解释性。传统方法存在两大缺陷：一是不同算法对数据特征敏感度不同，选择困难；二是缺乏自动化、上下文相关的解释。该框架通过集成十种独立检测算法，显著优于单一方法。同时，基于LLM的解释管道可自动生成关联真实历史事件的语境化叙述。对于私有或领域特定数据，采用检索增强生成（RAG）方案，使解释基于用户提供的文档。开源Python框架已在金融、政治科学和环境科学等领域验证其实际应用价值，将原始统计输出转化为分析师与决策者可行动的洞察。
原文摘要 · Abstract (English)
This paper introduces a novel changepoint detection framework that combines ensemble statistical methods with Large Language Models (LLMs) to enhance both detection accuracy and the interpretability of regime changes in time series data. Two critical limitations in the field are addressed. First, individual detection methods exhibit complementary strengths and weaknesses depending on data characteristics, making method selection non-trivial and prone to suboptimal results. Second, automated, contextual explanations for detected changes are largely absent. The proposed ensemble method aggregates results from ten distinct changepoint detection algorithms, achieving superior performance and robustness compared to individual methods. Additionally, an LLM-powered explanation pipeline automatically generates contextual narratives, linking detected changepoints to potential real-world historical events. For private or domain-specific data, a Retrieval-Augmented Generation (RAG) solution enables explanations grounded in user-provided documents. The open source Python framework demonstrates practical utility in diverse domains, including finance, political science, and environmental science, transforming raw statistical output into actionable insights for analysts and decision-makers.
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