--- dataset_info: - config_name: binary features: - name: sample_id dtype: int32 - name: question_id dtype: int32 - name: trajectory dtype: array3_d: shape: - 320 - 24 - 3 dtype: float32 - name: action_sequence sequence: - name: start dtype: float32 - name: end dtype: float32 - name: action dtype: string - name: action_sentence dtype: string length: 4 - name: textual_description dtype: string - name: question_type dtype: string - name: question dtype: string - name: answer_type dtype: string - name: answer_text dtype: string - name: answer dtype: class_label: names: '0': 'true' '1': 'false' splits: - name: val num_bytes: 780073603 num_examples: 6235 - name: test num_bytes: 765035777 num_examples: 6115 - name: train num_bytes: 6187058124 num_examples: 49454 download_size: 5725425107 dataset_size: 7732167504 - config_name: multi features: - name: sample_id dtype: int32 - name: question_id dtype: int32 - name: trajectory dtype: array3_d: shape: - 320 - 24 - 3 dtype: float32 - name: action_sequence sequence: - name: start dtype: float32 - name: end dtype: float32 - name: action dtype: string - name: action_sentence dtype: string length: 4 - name: textual_description dtype: string - name: question_type dtype: string - name: question dtype: string - name: answer_type dtype: string - name: answer_text dtype: string - name: options struct: - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: answer dtype: class_label: names: '0': '0' '1': '1' '2': '2' - name: options_sequence sequence: string length: 3 splits: - name: val num_bytes: 541952411 num_examples: 4326 - name: test num_bytes: 544471458 num_examples: 4346 - name: train num_bytes: 4375578410 num_examples: 34927 download_size: 4048318598 dataset_size: 5462002279 - config_name: open features: - name: sample_id dtype: int32 - name: question_id dtype: int32 - name: trajectory dtype: array3_d: shape: - 320 - 24 - 3 dtype: float32 - name: action_sequence sequence: - name: start dtype: float32 - name: end dtype: float32 - name: action dtype: string - name: action_sentence dtype: string length: 4 - name: textual_description dtype: string - name: question_type dtype: string - name: question dtype: string - name: answer_type dtype: string - name: answer_text dtype: string splits: - name: val num_bytes: 555326923 num_examples: 4439 - name: test num_bytes: 567832548 num_examples: 4539 - name: train num_bytes: 4455975352 num_examples: 35619 download_size: 4140639676 dataset_size: 5579134823 configs: - config_name: binary data_files: - split: val path: binary/val-* - split: test path: binary/test-* - split: train path: binary/train-* - config_name: multi data_files: - split: val path: multi/val-* - split: test path: multi/test-* - split: train path: multi/train-* - config_name: open data_files: - split: val path: open/val-* - split: test path: open/test-* - split: train path: open/train-* license: cc-by-4.0 task_categories: - question-answering language: - en pretty_name: QuAnTS size_categories: - 100K At present, there is no official leaderboard for this dataset. ## Dataset Generation Overview ![QuAnTS is generated in several steps: An action sequence is sampled ➀, where for each we sample five question and answer types ➁. For diversity, each of them is then instantiated from a sampled template ➂. The time series from the human motion diffusion ➃ is then combined with the QA-pair and auxiliary data ➄. Example QA pairs are shown below. Dice indicate randomized operations for dataset diversity.](doc/overview.png "Dataset generation overview") For details, please refer to the paper: *Under Review* ## Task and Format The primary task for the QuAnTS dataset is Time Series Question Answering. Given a time series of human skeleton trajectories and a question in natural language, the goal is to generate a correct answer. Answers are provided in one of the following formats: binary (Yes/No), multiple-choice (A/B/C), or open (free text). Additionally, to provide more training data for free-text answers, we provide entirely textual answers for all binary and multiple-choice questions. The ground truth action sequence or scene descriptions *may not* be used to answer the dataset — we provide them for debugging purposes only. The text in the dataset is in English. We provide fixed splits into training, validation, and test portions, where only the latter may be used to compare performance across different approaches. You are free to mix the training and validation splits as needed. ## Licensing, Citation, and Acknowledgments The QuAnTS dataset is licensed under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](ttps://creativecommons.org/licenses/by/4.0/ ) license. If you use the QuAnTS dataset in your research, please cite [the paper]([2511.05124](https://arxiv.org/abs/2511.05124)): ``` @misc{divo2025quantsquestionansweringtime, title={QuAnTS: Question Answering on Time Series}, author={Felix Divo and Maurice Kraus and Anh Q. Nguyen and Hao Xue and Imran Razzak and Flora D. Salim and Kristian Kersting and Devendra Singh Dhami}, year={2025}, eprint={2511.05124}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2511.05124}, } ``` The dataset was curated by a team of researchers from various institutions: * Felix Divo, Maurice Kraus, and Kristian Kersting (hessian.AI, DFKI, and the Centre for Cognitive Science) from Technische Universität Darmstadt. * Anh Q. Nguyen, Hao Xue, and Flora D. Salim from UNSW Sydney. * Imran Razzak from Mohamed bin Zayed University of Artificial Intelligence. * Devendra Singh Dhami from Eindhoven University of Technology.