Datasets:

TimeOmni-VL
/

TSUMM-Suite_Training

@@ -1,42 +1,195 @@
 ---
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*.parquet
-dataset_info:
-  features:
-  - name: sample_id
-    dtype: string
-  - name: task
-    dtype: string
-  - name: freq
-    dtype: string
-  - name: nvars
-    dtype: int32
-  - name: context_len
-    dtype: int32
-  - name: pred_len
-    dtype: int32
-  - name: periodicity
-    dtype: int32
-  - name: input_image
-    dtype: image
-  - name: target_image
-    dtype: image
-  - name: metadata_json
-    dtype: string
 ---
-# TimeOmni-VL Generation Samples
-This is the Hugging Face Dataset Viewer friendly version of the TimeOmni-VL
-forecasting and imputation samples.
-Columns:
-- `sample_id`: original sample directory name.
-- `task`: `forecasting` or `imputation`.
-- `input_image`: masked/source image.
-- `target_image`: full/target image.
-- `metadata_json`: original per-sample metadata serialized as JSON.

 ---
+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
+  - ts-image
+  - 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/YOUR_ORG/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/YOUR_ORG/TSUMM-SUITE">
+    <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/YOUR_ORG/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, 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/fig4.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}
+}
+```