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ShapeLex: Decoupling Local Shape Symbolization and Global Scale Modeling for Text-Controlled Time Series Generation — arXiv:2609.24003

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.24003
License (as archived): CC0-1.0
Archived: 2026-10-09T07:07:30.137Z
SHA-256 of the archived text: 8193b1535fc8f73b76ff8f5fa468c5f1639a66e38999aabd4c0da79682762f49
Stable URL of this page: https://metraipolis.com/documento/dc_c6d95bfcd71f

How to cite

ShapeLex: Decoupling Local Shape Symbolization and Global Scale Modeling for Text-Controlled Time Series Generation — arXiv:2609.24003 — arXiv (en), https://arxiv.org/abs/2609.24003 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_c6d95bfcd71f (Metraipolis archive dc_c6d95bfcd71f, sha256 8193b1535fc8f73b76ff8f5fa468c5f1639a66e38999aabd4c0da79682762f49)

BibTeX

@misc{dcc6d95bfcd71f,
  title = {ShapeLex: Decoupling Local Shape Symbolization and Global Scale Modeling for Text-Controlled Time Series Generation — arXiv:2609.24003},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.24003},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_c6d95bfcd71f; sha256 8193b1535fc8f73b76ff8f5fa468c5f1639a66e38999aabd4c0da79682762f49},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.24003. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Text-controlled time series generation aims to synthesize sequences that follow natural-language descriptions while remaining faithful to real data distributions. Existing paradigms often couple semantic understanding and sequence modeling in a single continuous latent space, lacking explicit local semantic anchors and separation between global continuous attributes and local discrete shapes. As a result, key local structures may be smoothed, missed, or misplaced. We propose Shape Lexicon (ShapeLex), which decouples text-to-sequence generation into discrete symbolization of local shapes and continuous modeling of global attributes. ShapeLex first induces a reusable vocabulary of discrete shape units, such as rises, spikes, and sharp drops, from training data, forming an interpretable symbolic space. An autoregressive generator then selects shapes according to the textual description, adjusts attributes such as position and duration, and composes them in temporal order into a shape skeleton. Finally, a mixture-density scale head models and samples the overall level and volatility to restore realistic global scale. Experiments on twelve public datasets, real user-written text, and downstream forecasting tasks show that ShapeLex generates series that better match real data distributions than existing methods. In addition, paired supervision is automatically synthesized from the learned vocabulary, avoiding annotation costs that grow with dataset size and improving scalability.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-22 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.24003 — 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 2026-10-09T06:58:09.360Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-22?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).