Datasets:

TimeOmni-VL
/

TSUMM_SUITE_Train

	---
	license: apache-2.0
	task_categories:
	- question-answering
	- visual-question-answering
	- image-to-image
	tags:
	- time series
	- time series understanding
	- time series generation
	- time series forecasting
	- time series imputation
	- multimodal
	- temporal reasoning
	- understanding-guided generation
	- timeomni-vl
	language:
	- en
	size_categories:
	- 10K<n<100K
	---

	# 🧱 TSUMM-SUITE Training Set

	<p align="left">
	<a href="https://arxiv.org/abs/2602.17149">
	<img
	src="https://img.shields.io/badge/TimeOmni--VL-Paper-red?logo=arxiv&logoColor=red"
	style="display: inline-block; vertical-align: middle;"
	alt="TimeOmni-VL Paper on arXiv"
	/>
	</a>
	<a href="https://huggingface.co/TimeOmni-VL/TimeOmni-VL">
	<img
	src="https://img.shields.io/badge/TimeOmni--VL-Model-yellow?logo=huggingface&logoColor=white"
	style="display: inline-block; vertical-align: middle;"
	alt="TimeOmni-VL Model on Hugging Face"
	/>
	</a>

	<a href="https://huggingface.co/datasets/TimeOmni-VL/TSUMM_SUITE_Train">
	<img
	src="https://img.shields.io/badge/TSUMM--SUITE-Dataset-orange?logo=huggingface&logoColor=white"
	style="display: inline-block; vertical-align: middle;"
	alt="TSUMM-SUITE Dataset on Hugging Face"
	/>
	</a>

	<a href="https://huggingface.co/spaces/anton-hugging/TimeOmni-VL">
	<img
	src="https://img.shields.io/badge/TimeOmni--VL-Demo-blue?logo=huggingface&logoColor=white"
	style="display: inline-block; vertical-align: middle;"
	alt="TimeOmni-VL Demo on Hugging Face Spaces"
	/>
	</a>

	<a href="https://github.com/AntonGuan/TimeOmni-VL" target="_blank" style="margin: 2px;">
	<img
	src="https://img.shields.io/badge/TimeOmni--VL-%20Code-536af5?logo=github&logoColor=white"
	style="display: inline-block; vertical-align: middle;"
	alt="TimeOmni-VL Code on GitHub"
	/>
	</a>
	</p>

	> This repository provides the training set of TSUMM-SUITE, a multimodal dataset for unified time series understanding and generation. It contains forecasting and imputation tasks for TS-image generation, together with six TS-image understanding tasks derived from the same time series instances. By aligning understanding tasks with generation tasks, TSUMM-SUITE enables temporal understanding to improve time series generation.

	## 🎨 Task Illustration

	<div align="center">
	<img src="https://raw.githubusercontent.com/AntonGuan/TimeOmni-VL/main/figs/task.png" width="100%"/>
	</div>

	<p><strong>TSUMM-SUITE covers two task families:</strong></p>
	<div style="margin-left:18px">

	<p><strong>Generation tasks</strong></p>
	<ul>
	<li><strong>Forecasting.</strong> Complete the masked future region of a TS-image and decode the generated image region back into future numerical values.</li>
	<li><strong>Imputation.</strong> Complete masked internal regions of a TS-image and decode the generated image region back into missing numerical values.</li>
	</ul>

	<p><strong>Understanding tasks</strong></p>
	<ul>
	<li><strong>QA1: Variable Counting.</strong> Count the number of variables encoded in a TS-image.</li>
	<li><strong>QA2: Variable Y-Range.</strong> Locate the vertical range of a specific variable.</li>
	<li><strong>QA3: Cycle Bounding Box.</strong> Locate the spatial region of a target cycle.</li>
	<li><strong>QA4: Mean Comparison.</strong> Compare average values across cycles.</li>
	<li><strong>QA5: Anomaly Detection.</strong> Identify and locate anomalous regions.</li>
	<li><strong>QA6: Trend Analysis.</strong> Analyze temporal patterns within a specified region.</li>
	</ul>

	</div>

	## 📊 Dataset Information

	### 1. Dataset Components

	<table style="width:100%;border-collapse:collapse;font-size:13px">
	<thead>
	<tr>
	<th style="padding:10px 7px;text-align:center;vertical-align:middle;font-weight:700;border-bottom:2px solid #2563eb;color:#2563eb">Component</th>
	<th style="padding:10px 7px;text-align:center;vertical-align:middle;font-weight:700;border-bottom:2px solid #2563eb;color:#2563eb">Task Family</th>
	<th style="padding:10px 7px;text-align:center;vertical-align:middle;font-weight:700;border-bottom:2px solid #2563eb;color:#2563eb">Size</th>
	<th style="padding:10px 7px;text-align:center;vertical-align:middle;font-weight:700;border-bottom:2px solid #2563eb;color:#2563eb">Description</th>
	</tr>
	</thead>
	<tbody>
	<tr>
	<td style="padding:8px 7px;text-align:center;vertical-align:middle;border-bottom:1px solid rgba(128,128,128,0.15);color:#2563eb">Forecasting</td>
	<td style="padding:8px 7px;text-align:center;vertical-align:middle;border-bottom:1px solid rgba(128,128,128,0.15)"><span style="display:inline-block;padding:3px 9px;border-radius:999px;background:rgba(37,99,235,0.10);color:#2563eb;font-weight:600">Generation</span></td>
	<td style="padding:8px 7px;text-align:center;vertical-align:middle;border-bottom:1px solid rgba(128,128,128,0.15);font-weight:700">40,000</td>
	<td style="padding:8px 7px;text-align:center;vertical-align:middle;border-bottom:1px solid rgba(128,128,128,0.15)">TS-image completion samples for future sequence generation</td>
	</tr>
	<tr>
	<td style="padding:8px 7px;text-align:center;vertical-align:middle;border-bottom:1px solid rgba(128,128,128,0.15);color:#2563eb">Imputation</td>
	<td style="padding:8px 7px;text-align:center;vertical-align:middle;border-bottom:1px solid rgba(128,128,128,0.15)"><span style="display:inline-block;padding:3px 9px;border-radius:999px;background:rgba(37,99,235,0.10);color:#2563eb;font-weight:600">Generation</span></td>
	<td style="padding:8px 7px;text-align:center;vertical-align:middle;border-bottom:1px solid rgba(128,128,128,0.15);font-weight:700">40,000</td>
	<td style="padding:8px 7px;text-align:center;vertical-align:middle;border-bottom:1px solid rgba(128,128,128,0.15)">TS-image completion samples for missing-value reconstruction</td>
	</tr>
	<tr>
	<td style="padding:8px 7px;text-align:center;vertical-align:middle;border-bottom:1px solid rgba(128,128,128,0.15);color:#2563eb">TS-image QA</td>
	<td style="padding:8px 7px;text-align:center;vertical-align:middle;border-bottom:1px solid rgba(128,128,128,0.15)"><span style="display:inline-block;padding:3px 9px;border-radius:999px;background:rgba(37,99,235,0.10);color:#2563eb;font-weight:600">Understanding</span></td>
	<td style="padding:8px 7px;text-align:center;vertical-align:middle;border-bottom:1px solid rgba(128,128,128,0.15);font-weight:700">9,409</td>
	<td style="padding:8px 7px;text-align:center;vertical-align:middle;border-bottom:1px solid rgba(128,128,128,0.15)">CoT-guided QA pairs for layout-level and signal-level TS-image understanding</td>
	</tr>
	<tr>
	<td style="padding:8px 7px;text-align:center;vertical-align:middle;border-bottom:1px solid rgba(128,128,128,0.15);color:#2563eb">TSR-Suite</td>
	<td style="padding:8px 7px;text-align:center;vertical-align:middle;border-bottom:1px solid rgba(128,128,128,0.15)"><span style="display:inline-block;padding:3px 9px;border-radius:999px;background:rgba(37,99,235,0.10);color:#2563eb;font-weight:600">Reasoning</span></td>
	<td style="padding:8px 7px;text-align:center;vertical-align:middle;border-bottom:1px solid rgba(128,128,128,0.15);font-weight:700">2,339</td>
	<td style="padding:8px 7px;text-align:center;vertical-align:middle;border-bottom:1px solid rgba(128,128,128,0.15)">CoT-guided temporal reasoning samples used to strengthen temporal reasoning</td>
	</tr>
	</tbody>
	</table>

	### 2. Data Fields

	Generation samples contain fields such as:

	- `sample_id`: unique sample identifier
	- `task_type`: `forecasting` or `imputation`
	- `instruction`: task instruction for generation
	- `source_image`: masked TS-image input
	- `target_image`: ground-truth completed TS-image
	- `target_series`: numerical target sequence
	- `generation_cot`: temporal reasoning used as generation condition
	- `metadata`: frequency, prediction length, mask ratio, number of variables, and related information

	Understanding samples contain fields such as:

	- `question_id`: unique QA identifier
	- `image`: TS-image input
	- `question`: natural-language question
	- `cot`: reasoning chain
	- `answer`: ground-truth answer
	- `qa_type`: one of QA1–QA6
	- `level`: `layout` or `signal`

	## 🚀 Usage

	```python
	from datasets import load_dataset

	dataset = load_dataset("YOUR_ORG/TSUMM-SUITE")

	forecasting = dataset["forecasting"]
	imputation = dataset["imputation"]
	understanding = dataset["understanding"]
	reasoning = dataset["reasoning"]
	```

	## 📐 Evaluation

	For forecasting and imputation, we report normalized Mean Absolute Scaled Error (nMASE). Lower values indicate better numerical generation quality.

	For TS-image understanding, we report normalized task-specific scores in the range of `[0, 1]`. Higher values indicate better understanding performance.

	For TSR-Suite reasoning tasks, we report Accuracy (ACC) for text-output tasks and Mean Absolute Error (MAE) for sequence-output tasks.

	## License

	TSUMM-SUITE is released under the Apache 2.0 license.

	## ✍️ Citation

	```bibtex
	@article{guan2026timeomni,
	title={TimeOmni-VL: Unified Models for Time Series Understanding and Generation},
	author={Guan, Tong and Pan, Sheng and Barthelemy, Johan and Li, Zhao and Cai, Yujun and Alippi, Cesare and Jin, Ming and Pan, Shirui},
	journal={arXiv preprint arXiv:2602.17149},
	year={2026}
	}
	```