mlsanityData to 14 Oct 2026

QuAnTS: Question Answering on Time Series

arXiv 2511.05124 ↗First posted 7 Nov 2025cs.LGcited by 5

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Abstract (arXiv)

Text offers intuitive access to information. This can, in particular, complement the density of numerical time series, thereby enabling improved interactions with time series models to enhance accessibility and decision-making. While the creation of question-answering datasets and models has recently seen remarkable growth, most research focuses on question answering (QA) on vision and text, with time series receiving minute attention. To bridge this gap, we propose a challenging novel time series QA (TSQA) dataset, QuAnTS, for Question Answering on Time Series data. Specifically, we pose a wide variety of questions and answers about human motion in the form of tracked skeleton trajectories. We verify that the large-scale QuAnTS dataset is well-formed and comprehensive through extensive experiments. Thoroughly evaluating existing and newly proposed baselines then lays the groundwork for a deeper exploration of TSQA using QuAnTS. Additionally, we provide human performances as a key reference for gauging the practical usability of such models. We hope to encourage future research on interacting with time series models through text, enabling better decision-making and more transparent systems.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 59 of its 95 references that are in the map. First posted on arXiv 7 Nov 2025.

Builds on

59 of 95 references.

References · 59

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38 more
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  15. 2024Large Language Models for Time Series: A Surveycited by 114
  16. 2024ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoningcited by 103
  17. 2021Question Answering Over Temporal Knowledge Graphscited by 99
  18. 2024Multi-Track Timeline Control for Text-Driven 3D Human Motion Generationcited by 91
  19. 2024Language Models Still Struggle to Zero-shot Reason about Time Seriescited by 88
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  25. 2023ECG-QA: A Comprehensive Question Answering Dataset Combined With Electrocardiogramcited by 54
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50 most cited of 59 All 59 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026ARFBench: Benchmarking Time Series Question Answering Ability for Software Incident Responsecited by 6
  2. 2026FactoryBench: Evaluating Industrial Machine Understandingcited by 2
  3. 2026TS-Skill: A Benchmark for Evaluating Analytical Skills in Time-Series Question Answeringcited by 1
  4. 2026A Cost-Effective Multimodal LLM Reasoning Framework for Question Answering over Irregular Clinical Time Seriescited by 1
  5. 2026Sonar-TS: Search-Then-Verify Natural Language Querying for Time Series Databasescited by 0