EvoTS-Agent：一种用于金融时间序列变点检测的自进化大语言模型智能体

中文摘要 AI 辅助

金融时间序列具有非平稳和异质的统计特性，使得变点检测颇具挑战，因为没有任何一种无监督算法能在所有资产和市场 regime 中表现一致。传统工作流程因此高度依赖专家驱动的模型选择、特征设计和超参数调优，限制了其可扩展性和适应性。我们提出 EvoTS-Agent，一种用于自主金融时间序列变点检测的、经验证引导的自进化大语言模型（LLM）智能体。EvoTS-Agent 首先开展精心策划的探索性数据分析，以表征数据集特性并初始化候选检测模型；随后通过三种互补算子进化可执行实验轨迹：“修订”算子利用当前最优解，“替代策略”算子在进展停滞时探索完全不同的建模方向，“重组”算子从高性能轨迹中整合互补证据。验证反馈在整个搜索过程中引导轨迹进化，使智能体能够根据每个数据集的统计特性调整其检测流程，同时保持可靠的优化。在四个基准数据集上开展的实验表明，EvoTS-Agent 始终优于现有的基于 LLM 的智能体，且在所有评估的 backbone LLM 中均保持 100% 的执行成功率。

英文摘要

Financial time series exhibit non-stationary and heterogeneous statistical properties, making change-point detection challenging because no single unsupervised algorithm performs consistently across assets and market regimes. Conventional workflows consequently depend heavily on expert-driven model selection, feature design, and hyperparameter tuning, limiting their scalability and adaptability. We propose EvoTS-Agent, a validation-guided self-evolving LLM agent for autonomous financial time-series change-point detection. EvoTS-Agent first performs curated exploratory data analysis to characterize dataset properties and initialize candidate detection models. It then evolves executable experiment trajectories through three complementary operators: \textit{Revision} exploits the current best solution, \textit{Alternative Strategy} explores fundamentally different modeling directions when progress stagnates, and \textit{Recombination} synthesizes complementary evidence from high-performing trajectories. Validation feedback guides trajectory evolution throughout the search, enabling the agent to adapt its detection pipeline to the statistical characteristics of each dataset while preserving reliable optimization. Experiments across four benchmark datasets demonstrate that EvoTS-Agent consistently outperforms existing LLM-based agents while maintaining a 100\% execution success rate across all evaluated backbone LLMs.

发表机构

• Alan Turing Institute（阿兰·图灵研究所）
• University of Oxford（牛津大学）
• National University of Singapore（新加坡国立大学）
• NatWest AI Research（国民西敏寺银行人工智能研究部）
• University of Edinburgh（爱丁堡大学）
• University College London（伦敦大学学院）

机构由 AI 辅助整理，请以论文原文为准。