arXiv:2606.16545v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly being used for automated decision-making systems in finance, healthcare, or environmental monitoring. Time series data are ubiquitous in these fields, yet hard to process automatically. Can time series be analyzed by LLM agents? We examine three approaches: providing the agent with raw numerical data, using the LLM as a coding agent, or a combination of both. In the coding agent setup, the model iteratively queries the data using Python code. Using two time series understanding benchmarks, we show tha
The rapid advancement of LLMs necessitates exploring their capabilities in complex, data-driven applications like time-series analysis for automated decision-making.
This research provides critical insights into the practical applicability and limitations of LLM agents in processing crucial time series data across vital sectors like finance and healthcare.
The understanding of how LLMs can effectively integrate with numerical data and leverage coding for analysis is evolving, potentially enabling more sophisticated automated systems.
- · AI developers
- · Financial services
- · Healthcare sector
- · Environmental monitoring
- · Companies reliant on traditional, manual time-series analysis methods
LLM coding agents demonstrate an early capability to interpret and act upon time-series data.
This capability leads to the automation of complex analytical tasks currently performed by human experts.
Financial and healthcare industries see increased efficiency and potentially new services driven by autonomous AI agents analyzing dynamic data.
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Read at arXiv cs.CL