lotsa_data

# LOTSA Data

The Large-scale Open Time Series Archive (LOTSA) is a collection of open time series datasets for time series forecasting.
It was collected for the purpose of pre-training Large Time Series Models.

See the [paper](https://arxiv.org/abs/2402.02592) and [codebase](https://github.com/SalesforceAIResearch/uni2ts) for more information.

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

If you're using LOTSA data in your research or applications, please cite it using this BibTeX:

**BibTeX:**

```markdown
@article{woo2024unified,
title={Unified Training of Universal Time Series Forecasting Transformers},
author={Woo, Gerald and Liu, Chenghao and Kumar, Akshat and Xiong, Caiming and Savarese, Silvio and Sahoo, Doyen},
journal={arXiv preprint arXiv:2402.02592},
year={2024}
}
```

## Ethical Considerations

This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP.

# LOTSA 数据集

大规模开放时间序列档案（Large-scale Open Time Series Archive, LOTSA）是专为时间序列预测任务打造的开放时间序列数据集合集，其构建初衷是为大规模时间序列模型（Large Time Series Models）的预训练提供支撑。

如需了解更多详情，请参阅[相关论文](https://arxiv.org/abs/2402.02592)与[代码仓库](https://github.com/SalesforceAIResearch/uni2ts)。

## 引用

<!-- 若有介绍该数据集的论文或博客文章，相关APA与Bibtex引用信息请置于此部分。 -->

若您在研究或应用中使用LOTSA数据集，请采用以下BibTeX格式进行引用：

**BibTeX:**

markdown
@article{woo2024unified,
title={Unified Training of Universal Time Series Forecasting Transformers},
author={Woo, Gerald and Liu, Chenghao and Kumar, Akshat and Xiong, Caiming and Savarese, Silvio and Sahoo, Doyen},
journal={arXiv preprint arXiv:2402.02592},
year={2024}
}

## 伦理考量

本次数据集发布仅用于支持学术研究用途。本项目所提供的模型、数据集与代码并未针对所有下游应用场景进行专门设计与评估。我们强烈建议用户在部署该模型前，针对其准确性、安全性与公平性等潜在问题进行充分评估与妥善处理。同时，我们鼓励用户充分考量人工智能的通用局限性，遵守适用法律法规，并在选择应用场景时遵循最佳实践，尤其是在错误或不当使用可能对民众生命、权利或安全造成重大影响的高风险场景中。如需了解更多应用场景相关的指导规范，请参阅本项目的AUP与AI AUP文件。