FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting

📰 News

🚩 Updates (2025-02-27) Initial version available on arXiv FinTSB.

🚩 Updates (2025-02-24) First version of the FinTSB code released.

🌟 Overview

FinTSB is a comprehensive and practical financial time series benchmark. Our goal is to address the three systemic limitations in the evaluation of the Financial Time Series Forecasting field.
➊ Failure to account for the full spectrum of stock movement patterns observed in dynamic financial markets (Diversity Gap).
➋ The absence of unified assessment protocols undermines the validity of cross-study performance comparisons (Standardization Deficit).
➌ Neglect of critical market structure factors, resulting in inflated performance metrics that lack practical applicability (Real-World Mismatch).

🛠 Prerequisites

Ensure you are using Python 3.10.16 and install the necessary dependencies by running:

pip install -r requirements.txt

📊 Prepare Datastes

Begin by downloading the required datasets. All datasets are conveniently available at https://quantapi.eastmoney.com/. After downloading the data, you should preprocess it as outlined in our paper (We will open source it as soon as the camera-ready version of the paper is published). Finally, create a separate folder named ./data and neatly organize all the CSV files as shown below:

FinTSB
└── extreme
    └── dataset_1.pkl
    └── dataset_2.pkl
    └── dataset_3.pkl
    └── dataset_4.pkl
    └── dataset_5.pkl
└── fall
    └── dataset_1.pkl
    └── dataset_2.pkl
    └── dataset_3.pkl
    └── dataset_4.pkl
    └── dataset_5.pkl
└── fluctuation
    └── dataset_1.pkl
    └── dataset_2.pkl
    └── dataset_3.pkl
    └── dataset_4.pkl
    └── dataset_5.pkl
└── rise
    └── dataset_1.pkl
    └── dataset_2.pkl
    └── dataset_3.pkl
    └── dataset_4.pkl
    └── dataset_5.pkl

💻 Training Pipline

The pipeline of FinTSB includes the Data layer, Training layer, Backtesting layer, and Feedback layer, as shown in the figure below.

All configs are located in ./configs. For instance, to train the LSTM model using the FinTSB dataset, simply run:

python train.py --config_file configs/config_lstm.yaml

After training:

• Your trained model will be safely stored in ./output/model.bin.
• Numerical results in .csv format and .pkl format can be found in ./output/backtest_result.csv and ./output/pred.pkl.
• A comprehensive summary of quantitative metrics is accessible in ./output/backtest_report.txt.
• A visualization is available in ./output/xxx.jpg.

📚 Citation

If you find this repo useful, please consider citing our paper as follows:

@article{hu2025fintsb,
  title={FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting}, 
  author={Yifan Hu and Yuante Li and Peiyuan Liu and Yuxia Zhu and Naiqi Li and Tao Dai and Shu-tao Xia and Dawei Cheng and Changjun Jiang},
  journal={arXiv preprint arXiv:2502.18834},
  year={2025},
}

@article{hu2025finmamba,
  title={FinMamba: Market-Aware Graph Enhanced Multi-Level Mamba for Stock Movement Prediction}, 
  author={Hu, Yifan and Liu, Peiyuan and Li, Yuante and Cheng, Dawei and Li, Naiqi and Dai, Tao and Bao, Jigang and Xia Shu-Tao},
  journal={arXiv preprint arXiv:2502.06707},
  year={2025}
}

@article{hu2025timefilter,
  title={TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time Series Forecasting},
  author={Hu, Yifan and Zhang, Guibin and Liu, Peiyuan and Lan, Disen and Li, Naiqi and Cheng, Dawei and Dai, Tao and Xia, Shu-Tao and Pan, Shirui},
  journal={arXiv preprint arXiv:2501.13041},
  year={2025}
}

@inproceedings{hu2025adaptive,
  title={Adaptive Multi-Scale Decomposition Framework for Time Series Forecasting},
  author={Hu, Yifan and Liu, Peiyuan and Zhu, Peng and Cheng, Dawei and Dai, Tao},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2025}
}

@inproceedings{bian2024multi,
  title={Multi-patch prediction: adapting language models for time series representation learning},
  author={Bian, Yuxuan and Ju, Xuan and Li, Jiangtong and Xu, Zhijian and Cheng, Dawei and Xu, Qiang},
  booktitle={Forty-first International Conference on Machine Learning},
  year={2024}
}

🙏 Acknowledgement

Special thanks to the following repositories for their invaluable code and datasets:

• QLib

📩 Contact

If you have any questions, please contact huyf0122@gmail.com or submit an issue.