BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis

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.

Setup

1. Create conda environment(Optional)

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

2. Install dependecies

Install the required packages

pip install -r requirements.txt

3. Download the data

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
│ .DS_Store
│
├─electricity
│
├─ETT-small
│
├─exchange_rate
│
├─illness
│
├─traffic
│
└─weather

4. Experimental setup

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

Main Results

1. Multivariate Results

2. Univariate Results

Train and Evaluate

1. Multivariate forecasting

sh M.sh

2. Univariate forecasting

sh S.sh

3. (Optional) Training logs and Weight files

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

Citation

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}
}

Contact

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

Acknowledgement

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

xiatian815 / basisformer Goto Github PK

basisformer's Introduction