ares89 / FEDformer

FEDformer (ICML 2022 under review)

Frequency Enhanced Decomposed Transformer (FEDformer) is more efficient than standard Transformer with a linear complexity to the sequence length.

Our empirical studies with six benchmark datasets show that compared with state-of-the-art methods, FEDformer can reduce prediction error by 14.8% and 22.6% for multivariate and univariate time series, respectively.

Get Started

1. Install Python 3.6, PyTorch 1.9.0.
2. Download data. You can obtain all the six benchmarks from xxxx.
3. Train the model. We provide the experiment scripts of all benchmarks under the folder ./scripts. You can reproduce the experiment results by:

bash ./scripts/run_M.sh
bash ./scripts/run_S.sh

Citation

If you find this repo useful, please cite our paper.

xxxxx

Contact

If you have any question or want to use the code, please contact xxx@xxxx .

Acknowledgement

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

https://github.com/thuml/Autoformer

https://github.com/zhouhaoyi/Informer2020

https://github.com/zhouhaoyi/ETDataset

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