A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models

2025-09-15

AI Summary

Existing time-series reasoning approaches often neglect temporal dynamics and fail to integrate intermediate evidence systematically. Method: This paper introduces a novel paradigm centered on “reasoning topology,” categorizing fundamental topological structures—direct one-step, linear chain, and branching reasoning—and establishing the first unified representation learning framework and taxonomy for time series grounded in these topologies. The approach integrates large language models, tool-augmented reasoning, multimodal perception, agent-based closed-loop execution, and decomposition-driven verification, enabling streaming inference, distribution shift adaptation, and cost-aware deployment. Contribution/Results: We present the first topology-driven framework covering analysis, explanation, causal inference, decision-making, and generation; propose design principles for trustworthy reasoning—ensuring traceability, verifiability, and self-correction; and unify cross-domain benchmarks with open-source resources to shift evaluation from static accuracy toward dynamic explainability, sustainability, and reliability.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Reasoning under Uncertainty: Uncertainty Representations
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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 making, and time series generation, while a compact tag set spans these axes and captures decomposition and verification, ensembling, tool use, knowledge access, multimodality, agent loops, and LLM alignment regimes. Methods and systems are reviewed across domains, showing what each topology enables and where it breaks down in faithfulness or robustness, along with curated datasets, benchmarks, and resources that support study and deployment (https://github.com/blacksnail789521/Time-Series-Reasoning-Survey). Evaluation practices that keep evidence visible and temporally aligned are highlighted, and guidance is distilled on matching topology to uncertainty, grounding with observable artifacts, planning for shift and streaming, and treating cost and latency as design budgets. We emphasize that reasoning structures must balance capacity for grounding and self-correction against computational cost and reproducibility, while future progress will likely depend on benchmarks that tie reasoning quality to utility and on closed-loop testbeds that trade off cost and risk under shift-aware, streaming, and long-horizon settings. Taken together, these directions mark a shift from narrow accuracy toward reliability at scale, enabling systems that not only analyze but also understand, explain, and act on dynamic worlds with traceable evidence and credible outcomes.

Problem

• Surveying reasoning and agentic systems for time series analysis
• Organizing literature by reasoning topology and field objectives
• Evaluating methods for reliability and scalability in dynamic settings

Innovation

• Time series reasoning with explicit intermediates
• Branch-structured reasoning for revision and aggregation
• Closed-loop testbeds under shift-aware streaming settings