---
license: cc-by-4.0
configs:
- config_name: analysis_questions
  data_files: "analysis_questions.csv"
  default: true
- config_name: multiple_choice
  data_files: "multiple_choice.csv"
---

# TSAIA: Time Series Analysis Instructional Assessment

**TSAIA** is an instruction-based evaluation dataset specifically designed for time series analysis and multiple choice tasks.

![Alt text](./task_categorization.png)

## 📚 Dataset Structure

The dataset comprises two subsets:

- **analysis_questions**: 904 samples
- **multiple_choice**: 150 samples

### Fields in `analysis_questions`:

- `question_id`: Unique identifier for each question
- `question_type`: Type of question (e.g., `easy_stock-future price`)
- `prompt`: Natural language description of the task
- `data_str`: Embedded time series data (typically stock prices)
- `executor_variables`: Definitions of variables available for model execution
- `ground_truth_data`: Reference answer or target output
- `context`: Contextual information for the task
- `constraint`: Constraints on output format or variable naming

### Fields in `multiple_choice`:

- `question_id`: Unique identifier for each question
- `question_type`: Type of question
- `prompt`: Natural language description of the task
- `options`: A list of multiple-choice options
- `answer`: The correct option(s)
- `data_info`: Description of the data
- `answer_info`: Description of the answer
- `executor_variables`: Definitions of variables available for model execution

## 🔧 Usage

For usage instructions and examples, please refer to the GitHub repository: [GitHub Repository](https://github.com/USC-Melady/TSAIA)

## 📄 License

This dataset is licensed under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license. You are free to use and distribute it, provided appropriate credit is given.

## 🤝 Citation and Contribution

If you utilize the TSAIA dataset in your research or projects, please cite it accordingly.

Contributions are welcome! Feel free to submit pull requests or open issues to suggest improvements or add new task samples.