Time Series Augmented Generation for Financial Applications
Summary: arXiv:2604.19633v1 Announce Type: new
Abstract
Evaluating the reasoning capabilities of Large Language Models (LLMs) for complex, quantitative financial tasks is a critical and unsolved challenge. Standard benchmarks often fail to isolate an agent’s core ability to parse queries and orchestrate computations. To address this, we introduce a novel evaluation methodology and benchmark designed to rigorously measure an LLM agent’s reasoning for financial time-series analysis.
Introduction
The financial sector increasingly relies on artificial intelligence to enhance decision-making processes. However, the ability of LLMs to effectively tackle complex financial questions remains uncertain. Traditional evaluation metrics do not sufficiently assess the reasoning capabilities of these models, particularly in quantitative contexts.
Methodology
To bridge this gap, we propose a new evaluation methodology and a benchmark specifically tailored for financial time-series analysis. Our approach, known as Time Series Augmented Generation (TSAG), allows LLM agents to delegate quantitative tasks to verifiable, external tools. This delegation is intended to enhance the accuracy and reliability of the outputs generated by LLMs.
Benchmark Design
Our benchmark consists of 100 carefully curated financial questions designed to evaluate multiple state-of-the-art (SOTA) agents, including:
- GPT-4o
- Llama 3
- Qwen2
The evaluation metrics focus on:
- Tool selection accuracy
- Faithfulness of responses
- Frequency of hallucination
Results
The results of our large-scale empirical study indicate that capable agents can achieve near-perfect accuracy in tool usage while maintaining minimal hallucination rates. These findings validate the effectiveness of the tool-augmented paradigm in enhancing the performance of LLMs in financial applications.
Contributions
Our primary contributions include:
- The development of a robust evaluation framework for LLMs in financial contexts.
- Empirical insights into the performance of various state-of-the-art agents.
- The public release of our benchmark to promote standardized research in the field of reliable financial AI.
Conclusion
In conclusion, the Time Series Augmented Generation framework presents a significant advancement in evaluating LLMs for financial applications. By rigorously assessing reasoning capabilities and tool integration, we aim to foster further developments in AI technologies that can reliably assist in complex financial decision-making processes.