mlsanityData to 7 Oct 2026

Evaluating System 1 vs. 2 Reasoning Approaches for Zero-Shot Time Series Forecasting: A Benchmark and Insights

arXiv 2503.01895 ↗First posted 27 Feb 2025cs.LG cs.AIcited by 11

On the map: LLM reasoning → Time series reasoning

Abstract (arXiv)

Reasoning ability is crucial for solving challenging tasks. With the advancement of foundation models, such as the emergence of large language models (LLMs), a wide range of reasoning strategies has been proposed, including test-time enhancements, such as Chain-ofThought, and post-training optimizations, as used in DeepSeek-R1. While these reasoning strategies have demonstrated effectiveness across various challenging language or vision tasks, their applicability and impact on time-series forecasting (TSF), particularly the challenging zero-shot TSF, remain largely unexplored. In particular, it is unclear whether zero-shot TSF benefits from reasoning and, if so, what types of reasoning strategies are most effective. To bridge this gap, we propose ReC4TS, the first benchmark that systematically evaluates the effectiveness of popular reasoning strategies when applied to zero-shot TSF tasks. ReC4TS conducts comprehensive evaluations across datasets spanning eight domains, covering both unimodal and multimodal with short-term and longterm forecasting tasks. More importantly, ReC4TS provides key insights: (1) Self-consistency emerges as the most effective test-time reasoning strategy; (2) Group-relative policy optimization emerges as a more suitable approach for incentivizing reasoning ability during post-training; (3) Multimodal TSF benefits more from reasoning strategies compared to unimodal TSF. Beyond these insights, ReC4TS establishes two pioneering starting blocks to support future zero-shot TSF reasoning research: (1) A novel dataset, TimeThinking, containing forecasting samples annotated with reasoning trajectories from multiple advanced LLMs, and (2) A new and simple test-time scaling-law validated on foundational TSF models enabled by self-consistency reasoning strategy. All data and code are publicly accessible at: https://github.com/AdityaLab/OpenTimeR

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Placed in Time series reasoning, one of 17 directions in LLM reasoning. Builds on 29 of its 72 references that are in the map. First posted on arXiv 27 Feb 2025.

Builds on

29 of 72 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024A Picture is Worth A Thousand Numbers: Enabling LLMs Reason about Time Series via Visualization9 Nov

Other references · 28

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
  3. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
  4. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  5. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
  6. 2023Qwen Technical Reportcited by 3,273
  7. 2025s1: Simple test-time scalingcited by 1,311
  8. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 906
  9. 2024Unified Training of Universal Time Series Forecasting Transformerscited by 588
  10. 2024MOMENT: A Family of Open Time-series Foundation Modelscited by 453
  11. 2024Training Language Models to Self-Correct via Reinforcement Learningcited by 405
  12. 2025Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Modelscited by 291
16 more
  1. 2024Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Expertscited by 280
  2. 2024A Survey on Knowledge Distillation of Large Language Modelscited by 276
  3. 2023Reward Design with Language Modelscited by 257
  4. 2023Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategiescited by 236
  5. 2024Deep Time Series Models: A Comprehensive Survey and Benchmarkcited by 191
  6. 2024GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluationcited by 188
  7. 2024Multi-Step Reasoning with Large Language Models, a Surveycited by 140
  8. 2024CriticBench: Benchmarking LLMs for Critique-Correct Reasoningcited by 88
  9. 2024From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Futurecited by 85
  10. 2024LLM Reasoners: New Evaluation, Library, and Analysis of Step-by-Step Reasoning with Large Language Modelscited by 70
  11. 2025From Tables to Time: Extending TabPFN-v2 to Time Series Forecastingcited by 49
  12. 2024TSI-Bench: Benchmarking Time Series Imputationcited by 33
  13. 2025Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoningcited by 33
  14. 2023Large Pre-trained time series models for cross-domain Time series analysis taskscited by 22
  15. 2024Decoding Time Series with LLMs: A Multi-Agent Framework for Cross-Domain Annotationcited by 21
  16. 2024TableTime: Reformulating Time Series Classification as Training-Free Table Understanding with Large Language Modelscited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 9; 2026: 2.

11 papers.

  1. 2025TimeXL: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loopcited by 39
  2. 2025Time Series Forecasting via Reasoning: A Slow-Thinking Approach with Reinforcement Fine-Tuned LLMscited by 34
  3. 2025A Survey of Reasoning and Agentic Systems in Time Series with Large Language Modelscited by 21
  4. 2025How Can Time Series Analysis Benefit From Multiple Modalities? A Survey and Outlookcited by 20
  5. 2025TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement Learningcited by 16
  6. 2025Optimal Self-Consistency for Efficient Reasoning with Large Language Modelscited by 8
  7. 2025TimeRecipe: A Time-Series Forecasting Recipe via Benchmarking Module Level Effectivenesscited by 6
  8. 2025Training-Free Time Series Classification via In-Context Reasoning with LLM Agentscited by 4
  9. 2026Time Series Reasoning via Process-Verifiable Thinking Data Synthesis and Scheduling for Tailored LLM Reasoningcited by 3
  10. 2025Tackling Time-Series Forecasting Generalization via Mitigating Concept Driftcited by 1
  11. 2026SenTSR-Bench: Thinking with Injected Knowledge for Time-Series Reasoningcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2024Implicit Reasoning in Deep Time Series Forecasting
  2. 2025Investigating Compositional Reasoning in Time Series Foundation Models
  3. 2025Can Slow-thinking LLMs Reason Over Time? Empirical Studies in Time Series Forecasting
  4. 2024LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting
  5. 2026TSRBench: A Comprehensive Multi-task Multi-modal Time Series Reasoning Benchmark for Generalist Models
  6. 2026Tracing the Evidence Behind Zero-Shot Time-Series Forecasting: A Source-First Taxonomy and Audit Framework
  7. 2025TimeOmni-1: Incentivizing Complex Reasoning with Time Series in Large Language Models
  8. 2024Towards Time Series Reasoning with LLMs