Datasets:

TimeOmni-VL
/

TSUMM-Suite_Training

---
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_Training">
    <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-Code-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. 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>

<p style="margin-top:13px;font-size:12px;opacity:0.85">
* Note: This release open-sources the forecasting and imputation generation data only. We do not open-source the TS-image QA (Understanding) and TSR-Suite (Reasoning) training data.
</p>

All generation samples are stored in a single `train` split (80,000 rows total). Each TS-image has resolution **896×896**. In the paper, the main TimeOmni-VL experiments use **5k samples per generation task** for training.

### 2. Data Fields

Generation samples contain fields such as:

- `sample_id`: unique sample identifier
- `task`: `forecasting` or `imputation`
- `freq`: time series frequency (e.g., `H`, `D`, `5T`)
- `nvars`: number of variables
- `context_len`: context length
- `pred_len`: prediction or imputation length
- `periodicity`: periodicity of the series
- `input_image`: masked TS-image input (896×896)
- `target_image`: ground-truth completed TS-image (896×896)
- `metadata_json`: JSON string with normalization, patch layout, mask ratio, and related metadata

## 🚀 Usage

```python
from datasets import load_dataset

dataset = load_dataset("TimeOmni-VL/TSUMM-Suite_Training")
train = dataset["train"]

forecasting = train.filter(lambda x: x["task"] == "forecasting")
imputation = train.filter(lambda x: x["task"] == "imputation")

print(f"Total: {len(train)}, Forecasting: {len(forecasting)}, Imputation: {len(imputation)}")
```

## 📐 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}
}
```