arXiv:2509.11575v3 Announce Type: replace Abstract: Time series reasoning treats time as a first-class axis and incorporates intermediate evidence directly into the answer. This survey defines the problem and organizes the literature by reasoning topology with three families: direct reasoning in one step, linear chain reasoning with explicit intermediates, and branch-structured reasoning that explores, revises, and aggregates. The topology is crossed with the main objectives of the field, including traditional time series analysis, explanation and understanding, causal inference and decision m
The rapid advancements in large language models necessitate a structured approach to integrating them with time series analysis, moving beyond simple applications.
This survey provides a foundational framework for understanding and developing agentic AI systems for complex, dynamic data, which is crucial for decision-making across industries.
The emergence of clear conceptual frameworks for applying LLMs to time series data will accelerate the development and deployment of autonomous reasoning agents.
- · AI development firms
- · Data scientists
- · Financial services
- · Industrial automation
- · Tasks requiring manual time series analysis
- · Legacy AI systems without agentic capabilities
Improved predictive accuracy and automated decision-making in dynamic environments.
Increased demand for specialized AI agents capable of complex reasoning and real-time adaptation.
Potential for fully autonomous operational systems across critical infrastructures, leading to efficiency gains but also new vulnerabilities.
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Read at arXiv cs.AI