README.md

# HiTSR Dataset

[![arXiv](https://img.shields.io/badge/arXiv-2604.17295-FF6B6B.svg)]([https://arxiv.org/abs/2504.XXXXX](https://arxiv.org/abs/2604.17295))

This is the official dataset repository of the ACL 2026 Findings paper: "LLaTiSA: Towards Difficulty-Stratified Time Series Reasoning from Visual Perception to Semantics".

A comprehensive **multimodal time series understanding and reasoning dataset** with multiple complexity levels.

## Overview

This dataset contains time series data paired with visual representations 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:

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** (e.g., GitHub, Imgur, or cloud storage) and update the URL prefixes in the commands above.

5. **The output JSON files will have the `images` field populated** with the correct URLs:
   ```json
   {
     "images": [
       "https://your-hosting.com/images/plots/plot_1.png",
       "https://your-hosting.com/images/numeric/num_1.png"
     ]
   }
   ```

**Notes:**
- Each script generates two types of images per sample: trend plots (`plot_*.png`) and high-density numeric grids (`num_*.png`)
- Use `--sample_ratio 1.0` to process all samples (default is 0.5)
- The scripts automatically update the `prompt` and `answer` fields based on the dataset level requirements

## 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.

## Dataset Creator

Created as part of the LLaTiSA project.

## Contact

For issues or questions regarding the dataset, please open an issue on the HuggingFace Hub.