Financial Time-Series Analysis
Revolutionizing Financial Time-Series Modelling with TS-Agent
In the dynamic world of financial markets, the ability to accurately model time-series data is paramount for informed decision-making. Traditional AutoML systems, while efficient, often fall short in adaptability and domain-specific reasoning for complex financial scenarios. This paper introduces TS-Agent, a groundbreaking modular agentic framework that leverages Large Language Models (LLMs) to automate and enhance financial time-series modelling workflows. By integrating structured reasoning, contextual memory, and iterative feedback, TS-Agent delivers unparalleled performance, interpretability, and auditability.
Key Takeaways:
- TS-Agent introduces a modular, agentic framework for financial time-series modeling.
- Utilizes LLMs for reasoning, memory management, and dynamic code generation.
- Formalizes the modeling pipeline as a structured, iterative decision process across model selection, code refinement, and fine-tuning.
- Incorporates structured knowledge banks (Case Bank, Refinement Knowledge Bank, Code Base) for domain-specific context.
- Employs feedback-driven online learning for adaptive refinement and robust debugging.
- Designed for auditable and debuggable processes, crucial for high-stakes financial environments.
- Outperforms state-of-the-art AutoML and agentic baselines in accuracy, robustness, and decision traceability on diverse financial tasks.
Quantifiable Impact
TS-Agent delivers concrete, measurable improvements across critical financial modeling metrics, ensuring superior performance and reliability.
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Improved Accuracy
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Success Rate
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Reduced Risk
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Correlation Score Boost
Deep Analysis & Enterprise Applications
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Challenges in Financial Time-Series Modeling
Financial markets generate massive, dynamic time-series data, making high-performing, interpretable, and auditable models a significant challenge. Existing AutoML systems, while streamlining development, often lack adaptability and domain-specific responsiveness. LLMs offer potential for flexible automation but need structured integration for high-stakes financial applications.
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of financial institutions struggle with data interpretation
Structured Agentic Workflows with LLMs
TS-Agent is a modular framework automating financial time-series modeling. It formalizes the process into iterative decision stages: model selection, code refinement, and fine-tuning. This is guided by contextual reasoning and experimental feedback, leveraging structured knowledge banks and adaptive learning.
TS-Agent Workflow Overview
Task Description
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Case Retrieval
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Model Pre-selection (Stage 1)
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Code Refinement (Stage 2)
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Results & Logs
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| Interpretability |
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| Auditing & Debugging |
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| Financial Performance Focus |
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| Knowledge Integration |
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| Learning & Refinement |
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Structured Knowledge Banks
TS-Agent utilizes a Case Bank for past solutions, a Financial Time-Series Code Base for executable models and metrics, and a Refinement Knowledge Bank for expert heuristics. This provides context-aware decision-making, improving interpretability and reducing error propagation.
Feedback-Driven Online Learning
The planner agent continuously updates its policy based on experimental outcomes and code execution logs. This enables adaptive refinement loops beyond static AutoML or naive LLM agents, offering consistent introspection and improvement.
Auditable and Debuggable Design
The modular architecture isolates code modifications, logs decisions and rationales, facilitating reproducibility, fault localization, and compliance auditing. This is critical for high-stakes financial AI deployment.
Superior Performance Across Financial Tasks
Empirical evaluations on diverse financial forecasting and synthetic data generation tasks demonstrate that TS-Agent consistently outperforms state-of-the-art AutoML and agentic baselines. It achieves superior accuracy, robustness, and decision traceability, critical for financial services.
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RMSE reduction on Exchange data
TS-Agent Forecasting Task Example
A case study showcased TS-Agent's ability to forecast U.S. stock prices. Starting from a scaffolded pipeline, TS-Agent performs model pre-selection via case-based reasoning (e.g., Autoformer and PatchTST). It then undergoes iterative refinement and fine-tuning, reducing MAPE from 3.41 to 1.86, demonstrating efficient and interpretable end-to-end automation.
Calculate Your Potential AI ROI
Estimate the significant cost savings and efficiency gains your enterprise could achieve by implementing structured agentic AI workflows. Adjust the parameters to see your projected annual impact.
Annual Cost Savings
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Hours Reclaimed Annually
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Your Enterprise AI Implementation Roadmap
A phased approach to integrate TS-Agent and structured agentic workflows into your financial operations, ensuring a smooth transition and maximum impact.
Phase 1: Discovery & Assessment
Understand current financial time-series challenges, data infrastructure, and define specific business objectives for AI integration. Identify key stakeholders and success metrics.
Phase 2: Pilot Program & Customization
Deploy a pilot TS-Agent instance on a critical financial task. Customize knowledge banks with proprietary data and financial models. Evaluate performance and gather feedback.
Phase 3: Integration & Scaling
Integrate TS-Agent with existing financial systems and workflows. Scale deployment across relevant departments, providing training and support for financial analysts and data scientists.
Phase 4: Continuous Optimization & Auditing
Establish a framework for continuous monitoring, feedback-driven refinement, and regular auditing to ensure compliance, optimal performance, and adaptive learning.
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