--- license: mit task_categories: [question-answering, multiple-choice, summarization] tags: [time-series, multimodal] pretty_name: dualTslm training data --- # DualTime-data
The in-house training corpora of *Twice Upon a Time: Complementary Representations for Time-Series Reasoning*, named as in the paper's training-data table. Raw JSON / JSONL with the series as number arrays; the charts and delay-embedding images are rendered at training time by the code repository. | Stage | File | Rows | Supervision | |---|---|---:|---| | A | `stageA/chatts_style_captions.jsonl` | 32,000 | caption | | A | `stageA/structural_captions.json` | 7,701 | caption | | B | `stageB/tsexam_perception.json` | 20,640 | multiple choice | | B | `stageB/numerical_corpus.json` | 11,000 | scalar answer | | B | `stageB/reasoning_corpus.json` | 100,000 | multiple choice, with rationales | The other training sources come from their own repositories: CaTS-Bench (`mhfisher/CaTSBench`, Stage A) and the ChatTS `ift` / `sft` sets (`ChatTSRepo/ChatTS-Training-Dataset`, Stage B). ## Usage ```bash hf download SequentialLearning/DualTime-data --repo-type dataset --local-dir data ``` `data/` is where the code repository's training scripts read the corpora (`STAGE=A bash scripts/train.sh`, `STAGE=B bash scripts/train.sh`). ## Licences `structural_captions`, `tsexam_perception` and `numerical_corpus` are generated with a fork of the TimeSeriesExam generator (MIT); `chatts_style_captions` is built on series from the ChatTS time-series generator (see that dataset's card); `reasoning_corpus` is this project's own synthetic data. ## Citation ```bibtex @misc{zisling2026twicetime, title={Twice Upon a Time: Complementary Representations for Time-Series Reasoning}, author={Hedi Zisling and Liran Nochumsohn and Ilan Naiman and Mihir Parmar and Omri Azencot}, year={2026}, eprint={2610.22327}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2610.22327}, } ```