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

# AAA-HiTSR Dataset

[**Paper**](https://huggingface.co/papers/2604.17295) | [**GitHub**](https://github.com/RainingNovember/LLaTiSA)

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:

```json
{
  "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](https://github.com/RainingNovember/LLaTiSA):

1. **Clone the data conversion repository:**
   ```bash
   git clone https://github.com/RainingNovember/LLaTiSA.git
   cd LLaTiSA/data_convert
   ```

2. **Install required dependencies:**
   ```bash
   pip install -r requirements.txt
   ```

3. **Run the appropriate conversion script based on dataset level:**
   
   **For Level 1 datasets:**
   ```bash
   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:**
   ```bash
   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:**
   ```bash
   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:

```bibtex
@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.