Survey of Reasoning and Agentic Systems for Time Series Analysis with Large Language Models
Researchers have published a comprehensive survey organizing methods for applying large language models to time series reasoning, categorizing approaches by reasoning topology (direct, linear chain, and branch-structured) and objectives (analysis, explanation, causal inference, and generation). The survey reviews how different reasoning structures enable various capabilities while identifying failure modes in faithfulness and robustness. This work matters because it provides guidance for building reliable, explainable AI systems that can analyze and act on temporal data at scale.
A new survey paper on arXiv systematically reviews how large language models can be applied to time series reasoning—treating time as a fundamental dimension and incorporating intermediate evidence into answers. The authors organize the literature across three reasoning topologies: direct one-step reasoning, linear chain reasoning with explicit intermediate steps, and branch-structured reasoning that explores, revises, and aggregates information. These topologies are cross-referenced with major objectives including traditional time series analysis, explanation and understanding, causal inference, decision-making, and time series generation. The survey identifies key technical approaches spanning decomposition, verification, ensembling, tool use, knowledge access, multimodality, agent loops, and LLM alignment strategies. The authors emphasize that effective systems must balance grounding and self-correction capabilities against computational cost and reproducibility, and they highlight the importance of evaluation practices that keep evidence visible and temporally aligned.
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- arXiv cs.AICenter
A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models
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