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AI Research
arXiv CS.AI·Jun 11·5 min read
A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models
TL;DR
Time series data, which captures information over time, often lacks effective reasoning capabilities in AI systems. This survey explores how Large Language Models (LLMs) can enhance reasoning and agentic systems for time series analysis.
✦ Why It Matters
Engineers can leverage LLMs to enhance the reasoning capabilities of their time series analysis systems.
Key Takeaways
- Explore three reasoning topologies for time series analysis.
- Focus on balancing computational cost with reasoning capacity.
- Utilize evaluation practices that ensure evidence visibility.
- Adopt frameworks that enhance reliability and understanding in dynamic data.
How It Works
The survey categorizes reasoning methods into three topologies: direct reasoning allows for immediate conclusions, linear chain reasoning incorporates intermediate steps for clarity, and branch-structured reasoning enables exploration and revision of conclusions. This structured approach helps in understanding complex time series data and supports various objectives like causal inference and decision-making.