Unlocking Time Series Insights with AI: Introducing QuAnTS for Natural Language Human Motion Anal...
By Felix Divo, Maurice Kraus, Anh Q. Nguyen, Hao Xue, Imran Razzak, Flora D. Salim, Kristian Kersting, Devendra Singh Dhami
Published on November 10, 2025| Vol. 1, Issue No. 1
Content Source
This is a curated briefing. The original article was published on cs.LG updates on arXiv.org.
Summary
This paper introduces QuAnTS (Question Answering on Time Series), a novel and challenging dataset designed to bridge the significant gap in research on Question Answering (QA) for time series data. While QA has seen growth in text and vision domains, time series have been largely overlooked. QuAnTS focuses specifically on human motion, using tracked skeleton trajectories to provide a diverse set of questions and answers. The dataset's comprehensive nature is verified through extensive experiments, and it provides baselines and human performance references to encourage future research into text-based interaction with time series models, ultimately aiming to enhance accessibility, decision-making, and system transparency.
Why It Matters
The introduction of QuAnTS marks a pivotal step towards democratizing access to and insights from complex time series data, a traditionally challenging domain requiring specialized analytical skills. By enabling natural language interaction with time series, this research significantly broadens the potential user base beyond data scientists, allowing domain experts, decision-makers, and even consumers to intuitively query and understand temporal patterns. This is particularly impactful for applications involving human motion - from healthcare and rehabilitation to sports analytics and robotics - where quick, natural language answers about movement patterns can drive more informed interventions and personalized feedback.
Fundamentally, QuAnTS represents a critical frontier in multimodal AI, pushing Large Language Models (LLMs) beyond their comfort zone of text and static images into the dynamic, numerical world of time series. The ability to 'talk' to time series data is not merely a convenience; it's a leap towards more transparent and interpretable AI systems. Users can ask 'why' a particular trend occurred or 'what' actions led to a specific outcome, moving beyond black-box predictions to interactive, explanatory AI. This approach fosters greater trust and empowers better decision-making by making complex temporal relationships accessible. For AI professionals, this dataset and its accompanying research highlight a lucrative area for innovation in multimodal model architectures, natural language processing for numerical data, and the development of intuitive user interfaces that bridge the human-data divide.