arXiv:2606.12481v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong reasoning and instruction-following capabilities, making them potentially powerful tools for time-series analysis. However, time series lie outside their native textual modality, raising a fundamental question: how should time series be represented so that LLMs can reason about them effectively? Existing work typically serializes raw numerical sequences or fine-tunes pre-trained LLMs on time-series data. These approaches place the burden of extracting temporal structure directly on the LLM,
The rapid advancement and widespread adoption of large language models are pushing researchers to address their limitations, particularly in non-textual data domains like time series.
This research addresses a fundamental challenge for LLMs in handling numerical, temporal data, unlocking new applications in finance, scientific research, and operational intelligence.
The ability of LLMs to analyze and reason about time series data moves beyond basic serialization, enabling more sophisticated understanding and predictive capabilities.
- · AI researchers
- · Financial modeling sector
- · Predictive maintenance industries
- · Healthcare analytics
- · Traditional time series analysis software without LLM integration
- · Data scientists relying solely on classical numerical methods
LLMs can interpret and generate insights from complex time-series data more effectively than before.
New AI-driven applications emerge in fields requiring dynamic data analysis, such as real-time market prediction and anomaly detection.
The integration of structured program representations for time series could lead to more robust and explainable AI models across scientific and industrial domains.
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Read at arXiv cs.AI