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TimeCap: A Scalable Open-Source Pipeline for Captioning Wearable Health Time-Series Data — arXiv:2610.21274

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.21274
License (as archived): CC0-1.0
Archived: 2026-10-16T10:02:11.726Z
SHA-256 of the archived text: 8d8551d9c088d5e8df89fdb0a4ca560fff464cc070e56a377595dab6038e6c34
Stable URL of this page: https://metraipolis.com/documento/dc_3dfb59bca9bf

How to cite

TimeCap: A Scalable Open-Source Pipeline for Captioning Wearable Health Time-Series Data — arXiv:2610.21274 — arXiv (en), https://arxiv.org/abs/2610.21274 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_3dfb59bca9bf (Metraipolis archive dc_3dfb59bca9bf, sha256 8d8551d9c088d5e8df89fdb0a4ca560fff464cc070e56a377595dab6038e6c34)

BibTeX

@misc{dc3dfb59bca9bf,
  title = {TimeCap: A Scalable Open-Source Pipeline for Captioning Wearable Health Time-Series Data — arXiv:2610.21274},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.21274},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_3dfb59bca9bf; sha256 8d8551d9c088d5e8df89fdb0a4ca560fff464cc070e56a377595dab6038e6c34},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.21274. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Time-series data generated continuously across industrial, environmental, and especially health domains produce multimodal signals whose scale and heterogeneity make their processing and interpretation particularly challenging. Time-Series Language Models (TSLMs) promise scalable interpretation of these signals, yet remain bottlenecked by a fundamental lack of text-annotated time series pairs that are signal-grounded, reproducible, and extensible across modalities and schemas. Existing paired corpora are either closed or limited in scale and modality coverage. Annotation with large language models could scale them up, but its outputs cannot be regenerated exactly, which prevents others from re-deriving, extending, or auditing the resulting corpus. To address this, we release TimeCap, an open deterministic captioning pipeline with three reusable extractor tiers and a cross-channel synthesis layer that operates directly on the signal with no language model in the loop, extensible to new schemas by writing channel definitions alone. This positions TimeCap as a reproducible mechanism for future dataset generation rather than a one-off release. Applying TimeCap to OpenMHC, the MyHeart Counts wearable dataset, yields MHCap, the largest open caption layer for multimodal wearable time-series data. MHCap spans 19 Apple HealthKit channels and 7,005,763 captions over 908,944 person-days. To showcase the impact of the corpus, we train OpenTSLM, a time-series language model, on MHCap and benchmark on health-outcome prediction. Caption-augmented training (OpenTSLM-Cap) outperforms a label-only baseline on 29 of 32 per-user binary evaluations across 8 tasks.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.CL, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2610.21274 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T08:44:47.745Z dalla pagina https://arxiv.org/list/cs.CL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).