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🎉 Getting Started | 💡 Overall Design

📦 Dataset | 🛠️ Scaler | 🧠 Model | 📉 Metrics | 🏃‍♂️ Runner | 📜 Config | 📜 Baselines

$\text{BasicTS}^{+}$ (Basic Time Series) is a benchmark library and toolkit designed for time series forecasting. It now supports a wide range of tasks and datasets, including spatial-temporal forecasting and long-term time series forecasting. It covers various types of algorithms such as statistical models, machine learning models, and deep learning models, making it an ideal tool for developing and evaluating time series forecasting models.

If you find this project helpful, please don't forget to give it a ⭐ Star to show your support. Thank you!

On one hand, BasicTS provides a unified and standardized pipeline, offering a fair and comprehensive platform for reproducing and comparing popular models.

On the other hand, BasicTS offers a user-friendly and easily extensible interface, enabling quick design and evaluation of new models. Users can simply define their model structure and easily perform basic operations.

You can find detailed tutorials in Getting Started. Additionally, we are collecting ToDo and HowTo items. If you need more features (e.g., additional datasets or benchmark models) or tutorials, feel free to open an issue or leave a comment here.

[!IMPORTANT] If you find this repository helpful for your work, please consider citing the following benchmarking paper:

@article{shao2024exploring,
 title={Exploring progress in multivariate time series forecasting: Comprehensive benchmarking and heterogeneity analysis},
 author={Shao, Zezhi and Wang, Fei and Xu, Yongjun and Wei, Wei and Yu, Chengqing and Zhang, Zhao and Yao, Di and Sun, Tao and Jin, Guangyin and Cao, Xin and others},
 journal={IEEE Transactions on Knowledge and Data Engineering},
 year={2024},
 publisher={IEEE}
}

🔥🔥🔥 The paper has been accepted by IEEE TKDE! You can check it out here. 🔥🔥🔥

✨ Highlighted Features

Fair Performance Review

Users can compare the performance of different models on arbitrary datasets fairly and exhaustively based on a unified and comprehensive pipeline.

Developing with BasicTS

🚀 Installation and Quick Start

For detailed instructions, please refer to the Getting Started tutorial.

📦 Supported Baselines

BasicTS implements a wealth of models, including classic models, spatial-temporal forecasting models, and long-term time series forecasting model:

You can find the implementation of these models in the baselines directory.

The code links (💻Code) in the table below point to the official implementations from these papers. Many thanks to the authors for open-sourcing their work!

📦 Supported Datasets

BasicTS support a variety of datasets, including spatial-temporal forecasting, long-term time series forecasting, and large-scale datasets.

📉 Main Results

See the paper Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis.

✨ Contributors

Thanks goes to these wonderful people (emoji key):

This project follows the all-contributors specification. Contributions of any kind welcome!

⭐ Star History

🔗 Acknowledgement

BasicTS is developed based on EasyTorch, an easy-to-use and powerful open-source neural network training framework.

📧 Contact

We invite you to join our official community to access comprehensive technical support.

Official Discord Server: Click here to join our Discord community

Official WeChat Group: