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[2602.17149] TimeOmni-VL: Unified Models for Time Series Understanding and Generation
Summary
TimeOmni-VL introduces a unified framework for time series understanding and generation, overcoming limitations of existing models by integrating visual and numerical data processing.
Why It Matters
This research addresses the gap between time series generation and understanding, proposing a novel approach that enhances both semantic comprehension and numerical accuracy. The implications are significant for fields relying on time series data, such as finance, healthcare, and IoT.
Key Takeaways
- TimeOmni-VL bridges the gap between numerical generation and semantic understanding in time series modeling.
- The framework employs bidirectional mapping between time series and images for improved data fidelity.
- TSUMM-Suite dataset introduces six understanding tasks paired with two generation tasks for comprehensive evaluation.
- The unified approach enhances both semantic understanding and numerical precision in time series analytics.
- This research sets a new standard for multimodal time series modeling, with potential applications across various industries.
Computer Science > Machine Learning arXiv:2602.17149 (cs) [Submitted on 19 Feb 2026] Title:TimeOmni-VL: Unified Models for Time Series Understanding and Generation Authors:Tong Guan, Sheng Pan, Johan Barthelemy, Zhao Li, Yujun Cai, Cesare Alippi, Ming Jin, Shirui Pan View a PDF of the paper titled TimeOmni-VL: Unified Models for Time Series Understanding and Generation, by Tong Guan and 7 other authors View PDF HTML (experimental) Abstract:Recent time series modeling faces a sharp divide between numerical generation and semantic understanding, with research showing that generation models often rely on superficial pattern matching, while understanding-oriented models struggle with high-fidelity numerical output. Although unified multimodal models (UMMs) have bridged this gap in vision, their potential for time series remains untapped. We propose TimeOmni-VL, the first vision-centric framework that unifies time series understanding and generation through two key innovations: (1) Fidelity-preserving bidirectional mapping between time series and images (Bi-TSI), which advances Time Series-to-Image (TS2I) and Image-to-Time Series (I2TS) conversions to ensure near-lossless transformations. (2) Understanding-guided generation. We introduce TSUMM-Suite, a novel dataset consists of six understanding tasks rooted in time series analytics that are coupled with two generation tasks. With a calibrated Chain-of-Thought, TimeOmni-VL is the first to leverage time series understanding as a...