--- 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

> **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

TSUMM-SUITE covers two task families:

Generation tasks

Forecasting. Complete the masked future region of a TS-image and decode the generated image region back into future numerical values.
Imputation. Complete masked internal regions of a TS-image and decode the generated image region back into missing numerical values.

Understanding tasks

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

## 📊 Dataset Information ### 1. Dataset Components

Component	Task Family	Size	Description
Forecasting	Generation	40,000	TS-image completion samples for future sequence generation
Imputation	Generation	40,000	TS-image completion samples for missing-value reconstruction
TS-image QA	Understanding	9,409	CoT-guided QA pairs for layout-level and signal-level TS-image understanding
TSR-Suite	Reasoning	2,339	CoT-guided temporal reasoning samples used to strengthen temporal reasoning

### 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} } ```