README.md

metadata

language:
  - en
task_categories:
  - image-text-to-text
tags:
  - time-series
  - multimodal
  - reasoning
  - vision-language-model

AAA-HiTSR Dataset

Paper | GitHub

A comprehensive multimodal time series understanding and reasoning dataset with multiple complexity levels, introduced in the paper "LLaTiSA: Towards Difficulty-Stratified Time Series Reasoning from Visual Perception to Semantics".

Overview

HiTSR (Hierarchical Time Series Reasoning) contains time series data paired with visual representations (plots and numeric grids) and natural language instructions for time series analysis tasks. The dataset is organized into 3 levels of complexity with corresponding train/test splits.

Dataset Statistics

• Level 1 (Basic): Single series analysis (min/max detection, trend analysis)

  • Training samples: 54,000
  • Test samples: Multiple variants (minmax, multiseries, startend, subseries)

• Level 2 (Intermediate): Multi-series analysis and relationships

  • Training samples: 45,632
  • Test categories: global, local, numerical

• Level 3 (Advanced): Complex reasoning and annotations

  • Training samples: 3,515
  • Test samples: Final test set

Data Structure

Each sample contains:

{
  "id": 1,
  "timeseries": [[float_values]],
  "prompt": "Multi-modal prompt with image tags",
  "answer": "Model answer to the question",
  "2img_prompt": "Detailed instructions for image interpretation",
  "prompt_1": "Variant 1 of the question",
  "prompt_2": "Variant 2 of the question",
  "answer_1": "Answer in format 1",
  "answer_2": "Answer in format 2",
  "images": ["image_url_1", "image_url_2"]
}

Key Fields

• id: Unique identifier for each sample
• timeseries: Array of time series values (floats)
• prompt: Main question/instruction with image references (e.g., <image>)
• answer: Expected model response
• 2img_prompt: Detailed instructions for interpreting high-density numeric grids
• prompt_1/prompt_2: Alternative question formats
• answer_1/answer_2: Alternative answer formats
• images: URLs to corresponding plot and numeric grid images

Image Generation

The images field in each sample contains URLs to visual representations of the time series data. To generate these images and populate the images field using the scripts provided in the official repository:

1. Clone the data conversion repository:

   git clone https://github.com/RainingNovember/LLaTiSA.git
   cd LLaTiSA/data_convert
   

2. Install required dependencies:

   pip install -r requirements.txt
   

3. Run the appropriate conversion script based on dataset level:

   For Level 1 datasets:

   python data_convert_l1.py \
     --input /path/to/level1_train.json \
     --output /path/to/level1_train_with_images.json \
     --plot_dir ./images/plots \
     --num_dir ./images/numeric \
     --plot_prefix "https://your-hosting.com/images/plots" \
     --num_prefix "https://your-hosting.com/images/numeric" \
     --sample_ratio 1.0
   

   For Level 2 datasets:

   python data_convert_l2.py \
     --input /path/to/level2_train.json \
     --output /path/to/level2_train_with_images.json \
     --plot_dir ./images/plots \
     --num_dir ./images/numeric \
     --plot_prefix "https://your-hosting.com/images/plots" \
     --num_prefix "https://your-hosting.com/images/numeric" \
     --sample_ratio 1.0
   

   For Level 3 datasets:

   python data_convert_l3.py \
     --input /path/to/level3_train.json \
     --output /path/to/level3_train_with_images.json \
     --plot_dir ./images/plots \
     --num_dir ./images/numeric \
     --plot_prefix "https://your-hosting.com/images/plots" \
     --num_prefix "https://your-hosting.com/images/numeric" \
     --sample_ratio 1.0
   

4. Upload generated images to a hosting service and update the URL prefixes in the commands above. The output JSON files will then have the images field populated with the correct URLs.

File Organization

AAA-HiTSR/
├── Train/
│   ├── l1_train_llatisa.json      (Level 1: 54,000 samples)
│   ├── l2_train_llatisa.json      (Level 2: 45,000+ samples)
│   └── l3_train_llatisa.json      (Level 3: 3,500+ samples)
└── Test/
    ├── l1_test_startend.json
    ├── l1_test_minmax.json
    ├── l1_test_subseries.json
    ├── l1_test_multiseries.json
    ├── l2_test_global.json
    ├── l2_test_local.json
    ├── l2_test_numerical.json
    └── l3_test.json

Task Types

Level 1 - Basic Time Series Analysis

• Finding maximum/minimum values and their indices
• Trend detection (start/end values)
• Subsequence identification

Level 2 - Multi-Series Analysis

• Global patterns and relationships
• Local anomalies and features
• Numerical reasoning over multiple series

Level 3 - Advanced Reasoning

• Complex queries requiring multi-step reasoning
• Real fine-tuning (RFT) and GRPO annotations

Citation

If you use this dataset in your research, please cite:

@article{llatisa2026,
  title={LLaTiSA: Towards Difficulty-Stratified Time Series Reasoning from Visual Perception to Semantics},
  author={Yueyang Ding, HaoPeng Zhang, Rui Dai, Yi Wang, Tianyu Zong, Kaikui Liu, Xiangxiang Chu},
  journal={arXiv preprint arXiv:2604.17295},
  year={2026}
}

Licensing

This dataset is released for research purposes.