Can LLM Coding Agents Reason About Time Series?
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 that agents with code access can outperform models processing raw data by up to 10%. However, even the best performing agent still answers about 22-...
| Authors | Filip Rechtorík, Ondřej Dušek, Zdeněk Kasner |
|---|---|
| Primary category | cs.CL |
| Published | 15 June 2026 |
| Last updated at source | 15 June 2026 |
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