This is the pytorch implementation of Basisformer in the Neurips paper: BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis

The architecture of our model(Basisformer) is shown as below:

Through extensive experiments on six datasets, we demonstrate that BasisFormer outperforms previous state-of-the-art methods by 11.04% and 15.78% respectively for univariate and multivariate forecasting tasks.

conda create -n basisformer -y python=3.8 
conda activate basisformer

Install the required packages

pip install -r requirements.txt

We follow the same setting as previous work. The datasets for all the six benchmarks can be obtained from [Autoformer]. The datasets are placed in the 'all_six_datasets' folder of our project. The tree structure of the files are as follows:

Basisformer\all_six_datasets
│
├─electricity
│
├─ETT-small
│
├─exchange_rate
│
├─illness
│
├─traffic
│
└─weather

The length of the historical input sequence is maintained at $96$(or $36$ for the illness dataset), whereas the length of the sequence to be predicted is selected from a range of values, i.e., ${96, 192, 336, 720}$ (${24, 36, 48, 60}$ for the illness dataset). Note that the input length is fixed to be 96 for all methods for a fair comparison. The evaluation is based on the mean squared error (MSE) and mean absolute error (MAE) metrics

sh script/M.sh

sh script/S.sh

Note: If you want to run multiple GPUs in parallel, you can replace script/M.sh and script/S.sh with script/M_parallel.sh and script/S_parallel.sh, respectively.

Training logs and weight files can be obtained from Baidu Netdisk(with an extract code: jqsr) or Google Drive

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

@inproceedings{ni2023basisformer,
  title={{Basisformer}: Attention-based Time Series Forecasting with Learnable and Interpretable Basis},
  author={Ni, Zelin and Yu, Hang and Liu, Shizhan and Li, Jianguo and Lin, Weiyao},
  booktitle={Advances in Neural Information Processing Systems},
  year={2023}
}

If there are any issues, please ask in the GitHub Issue module.

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

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