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paperarXivTrust 82 · PrimaryPublished 12d agoLive · 11d ago

EvoTS-Agent: A Self-Evolving LLM Agent for Financial Time Series Change Point Detection

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 characte

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  • FuzzySimilar title/name (fuzzy) · 59%AgentCore-8B

    Fuzzy title match (0.73): “EvoTS-Agent: A Self-Evolving LLM Agent for Financial Time Se” ≈ “AgentCore-8B”

  • LinkedLinked via arxiv author · 85%Lei Jiang

    EvoTS-Agent: A Self-Evolving LLM Agent for Financial Time Series Change Point Detection

  • LinkedLinked via arxiv author · 85%Juye Wei

    EvoTS-Agent: A Self-Evolving LLM Agent for Financial Time Series Change Point Detection

  • LinkedLinked via arxiv author · 85%Yangxinyu Xie

    EvoTS-Agent: A Self-Evolving LLM Agent for Financial Time Series Change Point Detection

  • LinkedLinked via arxiv author · 85%Jordan Langham-Lopez

    EvoTS-Agent: A Self-Evolving LLM Agent for Financial Time Series Change Point Detection

  • LinkedLinked via arxiv author · 85%Yifan Bao

    EvoTS-Agent: A Self-Evolving LLM Agent for Financial Time Series Change Point Detection

  • LinkedLinked via arxiv author · 85%Raad Khraishi

    EvoTS-Agent: A Self-Evolving LLM Agent for Financial Time Series Change Point Detection

  • LinkedLinked via arxiv author · 85%Yihao Ang

    EvoTS-Agent: A Self-Evolving LLM Agent for Financial Time Series Change Point Detection

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