Structured Agentic Workflows for Financial Time-Series Modeling with LLMs and Reflective Feedback

• URL: http://arxiv.org/abs/2508.13915v1
• Date: Tue, 19 Aug 2025 15:14:49 GMT
• Title: Structured Agentic Workflows for Financial Time-Series Modeling with LLMs and Reflective Feedback
• Authors: Yihao Ang, Yifan Bao, Lei Jiang, Jiajie Tao, Anthony K. H. Tung, Lukasz Szpruch, Hao Ni,
• Abstract summary: Time-series data is central to decision-making in financial markets, yet building high-performing, interpretable, and auditable models remains a major challenge.<n>textsfTSAgent is a modular agentic framework designed to automate and enhance time-series modeling for financial applications.
• Score: 16.04516547661581
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time-series data is central to decision-making in financial markets, yet building high-performing, interpretable, and auditable models remains a major challenge. While Automated Machine Learning (AutoML) frameworks streamline model development, they often lack adaptability and responsiveness to domain-specific needs and evolving objectives. Concurrently, Large Language Models (LLMs) have enabled agentic systems capable of reasoning, memory management, and dynamic code generation, offering a path toward more flexible workflow automation. In this paper, we introduce \textsf{TS-Agent}, a modular agentic framework designed to automate and enhance time-series modeling workflows for financial applications. The agent formalizes the pipeline as a structured, iterative decision process across three stages: model selection, code refinement, and fine-tuning, guided by contextual reasoning and experimental feedback. Central to our architecture is a planner agent equipped with structured knowledge banks, curated libraries of models and refinement strategies, which guide exploration, while improving interpretability and reducing error propagation. \textsf{TS-Agent} supports adaptive learning, robust debugging, and transparent auditing, key requirements for high-stakes environments such as financial services. Empirical evaluations on diverse financial forecasting and synthetic data generation tasks demonstrate that \textsf{TS-Agent} consistently outperforms state-of-the-art AutoML and agentic baselines, achieving superior accuracy, robustness, and decision traceability.

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