mlsanityData to 7 Oct 2026

Toward Reasoning-Centric Time-Series Analysis

arXiv 2510.13029 ↗First posted 14 Oct 2025cs.AIcited by 1

On the map: LLM reasoning → Time series reasoning

Abstract (arXiv)

Traditional time series analysis has long relied on pattern recognition, trained on static and well-established benchmarks. However, in real-world settings -- where policies shift, human behavior adapts, and unexpected events unfold -- effective analysis must go beyond surface-level trends to uncover the actual forces driving them. The recent rise of Large Language Models (LLMs) presents new opportunities for rethinking time series analysis by integrating multimodal inputs. However, as the use of LLMs becomes popular, we must remain cautious, asking why we use LLMs and how to exploit them effectively. Most existing LLM-based methods still employ their numerical regression ability and ignore their deeper reasoning potential. This paper argues for rethinking time series with LLMs as a reasoning task that prioritizes causal structure and explainability. This shift brings time series analysis closer to human-aligned understanding, enabling transparent and context-aware insights in complex real-world environments.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Time series reasoning, one of 17 directions in LLM reasoning. Builds on 13 of its 59 references that are in the map. First posted on arXiv 14 Oct 2025.

Builds on

13 of 59 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025OpenTSLM: Time-Series Language Models for Reasoning over Multivariate Medical Text- and Time-Series Data2 Oct

Other references · 12

  1. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
  2. 2025s1: Simple test-time scalingcited by 1,311
  3. 2024From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflectioncited by 93
  4. 2025TimeXL: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loopcited by 39
  5. 2024Causal Evaluation of Language Modelscited by 18
  6. 2025Inferring Events from Time Series using Language Modelscited by 10
  7. 2025CALM: A Framework for Continuous, Adaptive, and LLM-Mediated Anomaly Detection in Time-Series Streamscited by 4
  8. 2025Exploring Accuracy Law for Deep Time Series Forecasters: An Empirical Studycited by 4
  9. 2024From Real to Cloned Singer Identificationcited by 3
  10. 2024LOCAL: Learning with Orientation Matrix to Infer Causal Structure from Time Series Datacited by 2
  11. 2025HCR-Reasoner: Synergizing Large Language Models and Theory for Human-like Causal Reasoningcited by 1
  12. 2025SPEAR: Soft Prompt Enhanced Anomaly Recognition for Time Series Datacited by 1

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Chain-of-thought Reviewing and Correction for Time Series Question Answeringcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2026TimeSeriesExamAgent: Creating Time Series Reasoning Benchmarks at Scale
  2. 2024Can LLMs Understand Time Series Anomalies?
  3. 2024Understanding Why Large Language Models Can Be Ineffective in Time Series Analysis: The Impact of Modality Alignment
  4. 2025Achieving Time Series Reasoning Requires Rethinking Model Design, Tasks Formulation, and Evaluation
  5. 2025When LLM Meets Time Series: Can LLMs Perform Multi-Step Time Series Reasoning and Inference
  6. 2026Representing Time Series as Structured Programs for LLM Reasoning
  7. 2026From Recognition to Understanding: Unlocking Cognitive Time Series Reasoning with LLMs
  8. 2025Enhancing LLM Reasoning for Time Series Classification by Tailored Thinking and Fused Decision