First vision‑centric framework that unifies time‑series understanding and generation.
Bidirectional mapping (Bi‑TSI) achieves near‑lossless conversions between time series and images.
TSUMM‑Suite introduces six understanding tasks tied to two generation tasks in a single dataset.
Understanding‑guided generation employs a calibrated Chain‑of‑Thought to serve as a control signal.
Experimental results show superior performance in both understanding accuracy and numerical generation fidelity.
📖 Full Retelling
WHO: The research team led by Tong Guan and seven collaborators.
WHAT: A comprehensive framework called TimeOmni‑VL that unifies time series understanding and generation.
WHERE: Presented on the arXiv preprint server under Machine Learning (cs.LG) and Artificial Intelligence (cs.AI).
WHEN: Submitted on 19 February 2026.
WHY: To bridge the existing gap between numeric generation and semantic understanding in time‑series modeling by leveraging vision‑centric multimodal techniques.
TimeOmni‑VL introduces a fidelity‑preserving bidirectional mapping between time series and images (Bi‑TSI) to ensure near‑lossless conversions, and a new dataset, TSUMM‑Suite, which couples six understanding tasks with two generation tasks. With a calibrated Chain‑of‑Thought approach, the model uses understanding outputs as explicit control signals for high‑fidelity generation, achieving marked improvements in both semantic understanding and numerical precision.
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Deep Analysis
Why It Matters
TimeOmni-VL unifies time series generation and understanding by mapping series to images and back, enabling high‑fidelity generation guided by semantic tasks. This bridges a long‑standing gap in time‑series modeling and opens new possibilities for applications that require both accurate data synthesis and deep analytical insight.
Context & Background
Time‑series modeling has traditionally separated generation from understanding tasks
Vision‑centric multimodal models have succeeded in image‑text domains but not yet in time series
Existing time‑series methods lack a unified approach for simultaneous generation and semantic analysis
What Happens Next
The community may adopt TimeOmni‑VL for downstream analytics and synthesis tasks, integrate it into industrial pipelines, and expand the TSUMM‑Suite dataset. Further research will refine the bidirectional mapping, benchmark against existing methods, and potentially release open‑source code and pretrained models.
Frequently Asked Questions
What is the main innovation of TimeOmni‑VL?
It introduces a fidelity‑preserving bidirectional mapping between time series and images and uses understanding tasks as explicit control signals for generation.
How does TimeOmni‑VL improve generation quality?
By leveraging understanding‑guided generation, the model produces high‑fidelity numerical outputs that align with semantic analysis.
Will the dataset and code be publicly available?
The authors plan to release the TSUMM‑Suite dataset and code, enabling broader adoption and further research.
What are potential application areas?
Finance, health monitoring, IoT, and any domain requiring accurate time‑series synthesis and analysis.
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Original Source
--> Computer Science > Machine Learning arXiv:2602.17149 [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 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 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 an explicit control signal for high-fidelity generation. Experiments confirm that this unified approach significantly improves both semantic understanding and numerical precision, establishing a new frontier for multimodal time series modeling. Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2602.17149 [cs.LG] (or arXiv:2602.17149v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2602.17149 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submi...
