Structured Agentic Workflows for Financial Time-Series Modeling with LLMs and Reflective Feedback
This addresses the problem of inflexible and non-adaptive model development in financial markets, offering a solution for practitioners needing interpretable and auditable workflows, though it appears incremental as it builds on existing agentic and AutoML concepts.
The paper tackles the challenge of building high-performing, interpretable, and auditable models for financial time-series data by introducing TS-Agent, a modular agentic framework that automates modeling workflows, and it demonstrates that TS-Agent consistently outperforms state-of-the-art AutoML and agentic baselines in accuracy, robustness, and decision traceability.
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.