--- 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 TimeOmni-VL Paper on arXiv TimeOmni-VL Model on Hugging Face TSUMM-Suite Dataset on Hugging Face TimeOmni-VL Demo on Hugging Face Spaces TimeOmni-VL Code on GitHub

> **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

TSUMM-Suite covers two task families:

Generation tasks

Understanding tasks

## 📊 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

* 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.

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} } ```