mlsanityData to 7 Oct 2026

Inferring Events from Time Series using Language Models

arXiv 2503.14190 ↗First posted 18 Mar 2025cs.AIcited by 10

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Abstract (arXiv)

A common goal in analyzing time series data is to understand how events cause observed variations. We study whether Large Language Models (LLMs) can infer natural language events associated with time series data. We introduce an automated method for generating tasks that test a model's ability to reason about events associated with time series data based on sports data, and develop a new benchmarking method. In experiments spanning 18 LLMs, we prompt LLMs to infer unobserved events given time series data and observe surprising successes, even when providing minimal context. We then show that combining distillation with Reinforcement Learning (RL) can improve the performance for small language models to approach that of large proprietary reasoning models. All resources needed to reproduce our work are available: https://github.com/hartvigsen-group/GAMETime

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 27 of its 58 references that are in the map. First posted on arXiv 18 Mar 2025.

Builds on

27 of 58 references.

References · 27

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2024The Llama 3 Herd of Modelscited by 14,192
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  12. 2017Multitask learning and benchmarking with clinical time series datacited by 287
15 more
  1. 2023FinanceBench: A New Benchmark for Financial Question Answeringcited by 261
  2. 2023PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Financecited by 217
  3. 2024Large Language Models Can Learn Temporal Reasoningcited by 110
  4. 2024ForecastBench: A Dynamic Benchmark of AI Forecasting Capabilitiescited by 104
  5. 2024ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoningcited by 95
  6. 2024From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflectioncited by 93
  7. 2024ChatTime: A Unified Multimodal Time Series Foundation Model Bridging Numerical and Textual Datacited by 92
  8. 2024Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoningcited by 83
  9. 2024Language Models Still Struggle to Zero-shot Reason about Time Seriescited by 81
  10. 2024Context is Key: A Benchmark for Forecasting with Essential Textual Informationcited by 78
  11. 2024TimeSeriesExam: A time series understanding examcited by 47
  12. 2024Can LLMs Understand Time Series Anomalies?cited by 41
  13. 2024Can LLMs Serve As Time Series Anomaly Detectors?cited by 22
  14. 2024EconLogicQA: A Question-Answering Benchmark for Evaluating Large Language Models in Economic Sequential Reasoningcited by 21
  15. 2024Quantifying Qualitative Insights: Leveraging LLMs to Market Predictcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 5.

10 papers.

  1. 2025TimeOmni-1: Incentivizing Complex Reasoning with Time Series in Large Language Modelscited by 24
  2. 2025A Survey of Reasoning and Agentic Systems in Time Series with Large Language Modelscited by 21
  3. 2026TSRBench: A Comprehensive Multi-task Multi-modal Time Series Reasoning Benchmark for Generalist Modelscited by 14
  4. 2025BEDTime: A Unified Benchmark for Automatically Describing Time Seriescited by 11
  5. 2026STReasoner: Empowering LLMs for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learningcited by 7
  6. 2026Agentic Time Machine as an Infrastructure for Future-Event Forecastingcited by 2
  7. 2025Multimodal Language Models with Modality-Specific Experts for Financial Forecasting from Interleaved Sequences of Text and Time Seriescited by 1
  8. 2025Toward Reasoning-Centric Time-Series Analysiscited by 1
  9. 2026AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detectioncited by 0
  10. 2026LEAF: A Living Benchmark for Event-Augmented Forecastingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Thoth: Mid-Training Bridges LLMs to Time Series Understanding
  2. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
  3. 2025TsLLM: Augmenting LLMs for General Time Series Understanding and Prediction
  4. 2024Towards Time Series Reasoning with LLMs
  5. 2026TimeSeriesExamAgent: Creating Time Series Reasoning Benchmarks at Scale
  6. 2025When LLM Meets Time Series: Can LLMs Perform Multi-Step Time Series Reasoning and Inference
  7. 2024A Comprehensive Evaluation of Large Language Models on Temporal Event Forecasting
  8. 2025Conversational Time Series Foundation Models: Towards Explainable and Effective Forecasting