arXiv Artificial Intelligence

CodeTS: Verifiable Text-to-Time Series Generation via Executable Code

1 hour ago 15.09.2026 07:00 👁 1 views
CodeTS: Verifiable Text-to-Time Series Generation via Executable Code

Quick summary

arXiv:2609.15393v1 Announce Type: cross Abstract: Text-to-Time Series Generation (Text-to-TS) provides a promising paradigm for synthesizing time series from natural language, enabling scenario-specific generation when real observations are scarce or costly to acquire. However, existing methods typically lack an explicit mechanism for deriving generation logic from textual descriptions to guide time series synthesis. In this paper, we propose CodeTS, a verifiable framework that uses code as an intermediate generation interface, reformulating Text-to-TS generation as a Text-to-Code-to-TS proces

Key takeaways

  • arXiv:2609.15393v1 Announce Type: cross Abstract: Text-to-Time Series Generation (Text-to-TS) provides a promising paradigm for synthesizing time series from natural language, enabling scenario-specific generation when real observations are scarce or costly to acquire.
  • However, existing methods typically lack an explicit mechanism for deriving generation logic from textual descriptions to guide time series synthesis.
  • In this paper, we propose CodeTS, a verifiable framework that uses code as an intermediate generation interface, reformulating Text-to-TS generation as a Text-to-Code-to-TS proces

Why it matters

“CodeTS: Verifiable Text-to-Time Series Generation via Executable Code” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗