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Official implementation of the paper "FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective"

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FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective

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

Running the Codes

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

Citation

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}
}

Acknowledgement

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

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Official implementation of the paper "FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective"

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