This repo is the official Pytorch implementation of "FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective".

python main.py

• Covid: -- feature_size 55 -- embedding size 256 -- hidden size 512 -- batch size 4 --train_ratio 0.6 --val_ratio 0.2
• METR-LA: -- feature_size 207 -- embedding size 128 -- hidden size 256 -- batch size 32 --train_ratio 0.7 --val_ratio 0.2
• Traffic: feature_size 963 -- hidden size 128 -- hidden size 256 -- batch size 2 --train_ratio 0.7 --val_ratio 0.2
• ECG: feature_size 140 -- hidden size 128 -- hidden size 256 -- batch size 4 --train_ratio 0.7 --val_ratio 0.2
• Solar: feature_size 592 -- hidden size 128 -- hidden size 256 -- batch size 2 --train_ratio 0.7 --val_ratio 0.2
• Wiki: feature_size 2000 -- hidden size 128 -- hidden size 256 -- batch size 2 --train_ratio 0.7 --val_ratio 0.2
• Electricity: feature_size 370 -- hidden size 128 -- hidden size 256 -- batch size 32 --train_ratio 0.7 --val_ratio 0.2

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

@inproceedings{yi2023fouriergnn,
title={Fourier{GNN}: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective},
author={Kun Yi and Qi Zhang and Wei Fan and Hui He and Liang Hu and Pengyang Wang and Ning An and Longbing Cao and Zhendong Niu},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023}
}

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

1. StemGNN: https://github.com/microsoft/StemGNN
2. MTGNN: https://github.com/nnzhan/MTGNN
3. GraphWaveNet: https://github.com/nnzhan/Graph-WaveNet
4. AGCRN: https://github.com/LeiBAI/AGCRN