TimeOmni-VL: Unified Models For Time Series Understanding And Generation
Source
- Raw Markdown: paper_timeomni-vl-2026.md
- PDF: paper_timeomni-vl-2026.pdf
Core Claim
TimeOmni-VL unifies time-series understanding and generation through a vision-centric framework with fidelity-preserving time-series/image conversion and understanding-guided generation.
Key Contributions
- Introduces TimeOmni-VL as a vision-centric time-series UMM.
- Uses bidirectional Time Series-to-Image and Image-to-Time Series mappings designed for near-lossless transformation.
- Builds TSUMM-Suite with understanding and generation tasks.
- Uses calibrated CoT as an explicit control signal for high-fidelity generation.
Method Notes
TimeOmni-VL connects Unified Multimodal Models, Time-Series Foundation Models, and Synthetic Data For Time Series.
Evidence And Results
The abstract reports improved semantic understanding and numerical precision, while the paper positions TimeOmni-VL as a unified framework for forecasting, imputation, understanding, and reasoning tasks.
Limitations
The method relies on time-series/image conversion fidelity and UMM behavior over generated images. That makes it different from direct numerical or latent forecasting models.
Links Into The Wiki
Open Questions
- Can TS-image conversion remain faithful for very long or high-dimensional series?
- Does understanding-guided generation transfer outside the TSUMM-Suite task design?