mlsanityData to 7 Oct 2026

Achieving Time Series Reasoning Requires Rethinking Model Design, Tasks Formulation, and Evaluation

arXiv 2502.01477 ↗First posted 3 Feb 2025cs.LG cs.AIcited by 15

On the map: LLM reasoning → Time series reasoning

Abstract (arXiv)

Understanding time series data is fundamental to many real-world applications. Recent work explores multimodal large language models (MLLMs) to enhance time series understanding with contextual information beyond numerical signals. This area has grown from 7 papers in 2023 to over 580 in 2025, yet existing methods struggle in real-world settings. We analyze 20 influential works from 2025 across model design, task formulation, and evaluation, and identify critical gaps: methods adapt NLP techniques with limited attention to core time series properties; tasks remain restricted to traditional prediction and classification; and evaluations emphasize benchmarks over robustness, interpretability, or decision relevance. We argue that achieving time series reasoning requires rethinking model design, task formulation, and evaluation together. We define time series reasoning, outline challenges and future directions, and call on researchers to develop unified frameworks for robust, interpretable, and decision-relevant reasoning in real-world applications. The material is available at https://github.com/Eleanorkong/Awesome-Time-Series-Reasoning.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Placed in Time series reasoning, one of 17 directions in LLM reasoning. Builds on 23 of its 115 references that are in the map. First posted on arXiv 3 Feb 2025.

Builds on

23 of 115 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Towards Time Series Reasoning with LLMs17 Sep
  2. 2024Implicit Reasoning in Deep Time Series Forecasting17 Sep

Other references · 21

  1. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 1,981
  2. 2023A Survey on Multimodal Large Language Modelscited by 898
  3. 2022Transformers in Time Series: A Surveycited by 489
  4. 2024MM-LLMs: Recent Advances in MultiModal Large Language Modelscited by 277
  5. 2023Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Futurecited by 184
  6. 2023TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecastingcited by 170
  7. 2023LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasterscited by 127
  8. 2024A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challengescited by 86
  9. 2024Context is Key: A Benchmark for Forecasting with Essential Textual Informationcited by 78
  10. 2024A Survey on Diffusion Models for Time Series and Spatio-Temporal Datacited by 66
  11. 2024Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMscited by 48
  12. 2024TimeSeriesExam: A time series understanding examcited by 47
9 more
  1. 2024Can LLMs Understand Time Series Anomalies?cited by 41
  2. 2025Reasoning Language Models: A Blueprintcited by 41
  3. 2024Using Counterfactual Tasks to Evaluate the Generality of Analogical Reasoning in Large Language Modelscited by 38
  4. 2024A Survey on Generative AI and LLM for Video Generation, Understanding, and Streamingcited by 37
  5. 2024TSI-Bench: Benchmarking Time Series Imputationcited by 33
  6. 2025Decision-informed Neural Networks with Large Language Model Integration for Portfolio Optimizationcited by 14
  7. 2024Electrocardiogram-Language Model for Few-Shot Question Answering with Meta Learningcited by 4
  8. 2025CENTS: Generating synthetic electricity consumption time series for rare and unseen scenarioscited by 3
  9. 2024Are Large Language Models Useful for Time Series Data Analysis?cited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 9; 2026: 6.

15 papers.

Sort:

By area:

  1. 2025Time-MQA: Time Series Multi-Task Question Answering with Context Enhancementcited by 74
  2. 2025Multi-modal Time Series Analysis: A Tutorial and Surveycited by 24
  3. 2025TimeOmni-1: Incentivizing Complex Reasoning with Time Series in Large Language Modelscited by 24
  4. 2025A Survey of Reasoning and Agentic Systems in Time Series with Large Language Modelscited by 21
  5. 2025TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement Learningcited by 16
  6. 2025Harnessing Vision-Language Models for Time Series Anomaly Detectioncited by 9
  7. 2025TS-Agent: Understanding and Reasoning Over Raw Time Series via Iterative Insight Gatheringcited by 7
  8. 2025MLLM4TS: Leveraging Vision and Multimodal Language Models for General Time-Series Analysiscited by 4
  9. 2026Adaptive Time Series Reasoning via Segment Selectioncited by 3
  10. 2026TSRouter: Dynamic Modality-Model Selection for Time Series Reasoningcited by 2
  11. 2025UniDiff: A Unified Diffusion Framework for Multimodal Time Series Forecastingcited by 1
  12. 2026Multimodal Collaborative Debate for Zero-Shot Time Series Reasoningcited by 1
3 more
  1. 2026Rationale-Grounded In-Context Learning for Time Series Reasoning with Multimodal Large Language Modelscited by 0
  2. 2026What If TSF: Reframing Time Series Forecasting as Scenario-Guided Multimodal Forecastingcited by 0
  3. 2026AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detectioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026TimeSeriesExamAgent: Creating Time Series Reasoning Benchmarks at Scale
  2. 2024Language Models Still Struggle to Zero-shot Reason about Time Series
  3. 2026From Recognition to Understanding: Unlocking Cognitive Time Series Reasoning with LLMs
  4. 2025TimeSense:Making Large Language Models Proficient in Time-Series Analysis
  5. 2024ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning
  6. 2025Towards Interpretable Time Series Foundation Models
  7. 2025Toward Reasoning-Centric Time-Series Analysis
  8. 2026Thoth: Mid-Training Bridges LLMs to Time Series Understanding