Time series forecasting is a critical demand for real applications. Enlighted by the classic time series analysis and stochastic process theory, we propose the FAITH as a general series forecasting model paper .

FAITH captures inter-channel relationships and temporal global information in the sequence. Extensive experiments on 6 benchmarks for long-term forecasting and 5 benchmarks for short-term forecasting demonstrate that FAITH outperforms existing models in many fields, such as electricity, weather and traffic, proving its effectiveness and superiority both in long-term and short-term time series forecasting tasks.

1. pip install -r requirement.txt
2. Dataset. All the six long-term benchmark datasets can be obtained from Google Drive .
3. Reproducibility. We provide the experiment scripts under the folder ./scripts. You can reproduce the experiments results by:

   bash run_all.sh
   

If you have any questions, please contact 1284042551@qq.com. Welcome to discuss together.

If you find this repo useful, please cite our paper

@article{FAITH,
  title={FAITH :Frequency-domain Attention In Two Horizon for TSF},
  year={2024}
}

We appreciate the following github repos a lot for their valuable code base or datasets:

https://zhuanlan.zhihu.com/p/603468264

https://github.com/MAZiqing/FEDformer

https://github.com/thuml/Autoformer

https://github.com/zhouhaoyi/Informer2020

https://github.com/zhouhaoyi/ETDataset

https://github.com/laiguokun/multivariate-time-series-data

https://github.com/Zero-coder/MLGN?tab=readme-ov-file